Chaotic exploration and learning of locomotion behaviours

We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage

Preparation, Characterization and DFT Studies of Some New N-Nitrosocarbamates and N-Nitrosoureas

We are presenting the preparation, characterization and density functional theory (DFT) studies {B3LYP/6-31+G(d)) of several reiated classes of N-nitrosocarbamates and N-nitrosoureas. The iong-range goal is the design and preparation of compounds, which would undergo photochemical or hydrolytic decomposition, to yield stabilized cyclic cations that can serve as alkylating agents at various nucleophilic centers, including DNA bases

Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction.

Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children's social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a "mental model" of the robot, tailoring the tutoring to the robot's performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot's bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance

Information Assurance through Binary Vulnerability Auditing

The goal of this research is to develop improved methods of discovering vulnerabilities in software. A large volume of software, from the most frequently used programs on a desktop computer, such as web browsers, e-mail programs, and word processing applications, to mission-critical services for the space shuttle, is unintentionally vulnerable to attacks and thus insecure. By seeking to improve the identification of vulnerabilities in software, the security community can save the time and money necessary to restore compromised computer systems. In addition, this research is imperative to activities of national security such as counterterrorism. The current approach involves a systematic and complete analysis of the low-level organization of software systems in stark contrast to existing approaches which are either ad-hoc or unable to identify all buffer overflow vulnerabilities. The scope of this project is to develop techniques for identifying buffer overflows in closed-source software where only the software’s executable code is available. These techniques use a comprehensive analysis of the software system’s flow of execution called binary vulnerability auditing. Techniques for binary vulnerability auditing are grounded in science and, while unproven, are more complete than traditional ad-hoc approaches. Since there are several attack vectors in software, this research will focus on buffer overflows, the most common class of vulnerability

Behaviour and its consequences

In this thesis, I have examined the behaviour and some of its neural underpinnings of a ‘model’ animal, the tadpoles and froglets of Xenopus laevis, at different levels of description and detail. At a macroscopic level, I investigated the animals’ movements in a very simple space. Zooming in, I looked at locomotion in freely and fictively swimming animals as well as at some of the sensory and motor consequences of locomotion. For many of these projects, I tested not only one particular developmental stage but a range of stages, allowing me to test for changes in behaviour with development. Methodologically, I employed video tracking to quantify movements in space over a longer period of time, as well as at a higher temporal and spatial resolution for short periods to record head movements during swimming. Semi-intact in vitro preparations of tadpoles were used to examine fictive locomotion and its consequences using electrophysiological recordings of peripheral nerves. Movements in space remained fairly similar over development, from small tadpoles to froglets, with all animals following the walls in a square environment, although the strength of wall following (WF) increased with growth. Tentacles, which are putatively mechanosensory appendages that large tadpoles temporarily possess, did not play any role for the strength of WF. WF was passive at all developmental stages, meaning that the animals never actively turned at a convex curvature to follow the wall, but instead went straight and left the wall. This implies that WF is unlikely to serve a defensive or spatial function. Looking specifically at locomotion in tadpoles showed that these animals commonly swim at 20 - 40 mm/s forward speeds, and move their heads left to right at up to 2500°/s angular velocities. These velocities decrease with development, probably because swimming frequency also decreases, from about 8 to about 5 Hz. Developmentally appropriate swimming frequencies are also seen in fictive swimming when the animals are deprived of normal sensory feedback. The mechanisms behind the developmental decrease in swimming frequency remain to be elucidated; biomechanical factors might well play a role. The left- right head oscillations during swimming also represent vestibular self-stimulation, which reaches amplitudes that are much higher than any of the stimuli used in sensory vestibular experiments. Another consequence of locomotion was observed in large tadpoles with tentacles: These tentacles are retracted during swimming, via a locomotor corollary discharge from the spinal cord. What I have shown in this thesis is first, that navigational behaviour of X. laevis in a simple laboratory setting seems to be mainly driven and constrained by the environment. Second, I have quantified head movements during swimming and therefore vestibular reafference, and found a developmental decrease in the swimming frequency. Finally, I uncovered an unusual effect of locomotion, namely the retraction of the tentacles during swimming. Together, these studies deepen the understanding of behaviour and its consequences in X. laevis

Abstracts and Program for the Annual Meeting of the Georgia Academy of Science, 2017

The annual meeting of the Georgia Academy of Science took place March 24–25, 2017, at Young Harris College in Young Harris, Georgia. The keynote speaker was Dr. Bill Newsome, investigator at the Howard Hughes Medical Institute and Professor of Neurobiology at Stanford University School of Medicine. His presentation was entitled “Understanding the Brain: the Path Forward.” Additional presentations were provided by members of the Academy who represented the following sections: I. Biological Sciences, II. Chemistry, III. Earth & Atmospheric Sciences, IV. Physics, Mathematics, Computer Science, Engineering & Technology, V. Biomedical Sciences, VI. Philosophy & History of Science, VII. Science Education, and VIII. Anthropology