Controllability of structural brain networks.

Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function

Clustering Arabic Tweets for Sentiment Analysis

The focus of this study is to evaluate the impact of linguistic preprocessing and similarity functions for clustering Arabic Twitter tweets. The experiments apply an optimized version of the standard K-Means algorithm to assign tweets into positive and negative categories. The results show that root-based stemming has a significant advantage over light stemming in all settings. The Averaged Kullback-Leibler Divergence similarity function clearly outperforms the Cosine, Pearson Correlation, Jaccard Coefficient and Euclidean functions. The combination of the Averaged Kullback-Leibler Divergence and root-based stemming achieved the highest purity of 0.764 while the second-best purity was 0.719. These results are of importance as it is contrary to normal-sized documents where, in many information retrieval applications, light stemming performs better than root-based stemming and the Cosine function is commonly used

Clustering Arabic Tweets for Sentiment Analysis

The focus of this study is to evaluate the impact of linguistic preprocessing and similarity functions for clustering Arabic Twitter tweets. The experiments apply an optimized version of the standard K-Means algorithm to assign tweets into positive and negative categories. The results show that root-based stemming has a significant advantage over light stemming in all settings. The Averaged Kullback-Leibler Divergence similarity function clearly outperforms the Cosine, Pearson Correlation, Jaccard Coefficient and Euclidean functions. The combination of the Averaged Kullback-Leibler Divergence and root-based stemming achieved the highest purity of 0.764 while the second-best purity was 0.719. These results are of importance as it is contrary to normal-sized documents where, in many information retrieval applications, light stemming performs better than root-based stemming and the Cosine function is commonly used

First CLIPS Conference Proceedings, volume 2

The topics of volume 2 of First CLIPS Conference are associated with following applications: quality control; intelligent data bases and networks; Space Station Freedom; Space Shuttle and satellite; user interface; artificial neural systems and fuzzy logic; parallel and distributed processing; enchancements to CLIPS; aerospace; simulation and defense; advisory systems and tutors; and intelligent control

Design and evaluation of a biologically-inspired cloud elasticity framework

The elasticity in cloud is essential to the effective management of computational resources as it enables readjustment at runtime to meet application demands. Over the years, researchers and practitioners have proposed many auto-scaling solutions using versatile techniques ranging from simple if-then-else based rules to sophisticated optimisation, control theory and machine learning based methods. However, despite an extensive range of existing elasticity research, the aim of implementing an efficient scaling technique that satisfies the actual demands is still a challenge to achieve. The existing methods suffer from issues like: (1) the lack of adaptability and static scaling behaviour whilst considering completely fixed approaches; (2) the burden of additional computational overhead, the inability to cope with the sudden changes in the workload behaviour and the preference of adaptability over reliability at runtime whilst considering the fully dynamic approaches; and (3) the lack of considering uncertainty aspects while designing auto-scaling solutions. In this paper, we aim to address these issues using a holistic biologically-inspired feedback switch controller. This method utilises multiple controllers and a switching mechanism, implemented using fuzzy system, that realises the selection of suitable controller at runtime. The fuzzy system also facilitates the design of qualitative elasticity rules. Furthermore, to improve the possibility of avoiding the oscillatory behaviour (a problem commonly associated with switch methodologies), this paper integrates a biologically-inspired computational model of action selection. Lastly, we identify seven different kinds of real workload patterns and utilise them to evaluate the performance of the proposed method against the state-of-the-art approaches. The obtained computational results demonstrate that the proposed method results in achieving better performance without incurring any additional cost in comparison to the state-of-the-art approaches

Control Theoretic Analysis of Human Brain Networks

The brain is a complex system with complicated structures and entangled dynamics. Among the various approaches to investigating the brain\u27s mechanics, the graphical method provides a successful framework for understanding the topology of both the structural and functional networks, and discovering efficient diagnostic biomarkers for cognitive behaviors, brain disorders and diseases. Yet it cannot explain how the structure affects the functionality and how the brain tunes its transition among multiple states to manipulate the cognitive control. In my dissertation, I propose a novel framework of modeling the mechanics of the cognitive control, which involves in applying control theory to analyzing the brain networks and conceptually connecting the cognitive control with the engineering control. First, I examine the energy distribution among different states via combining the energetic and structural constraints of the brain\u27s state transition in a free energy model, where the interaction between regions is explicitly informed by structural connectivity. This work enables the possibility of achieving a whole view of the brain\u27s energy landscape and preliminarily indicates the feasibility of control theory to model the dynamics of cognitive control. In the following work, I exploit the network control theory to address two questions about how the large-scale circuitry of the human brain constrains its dynamics. First, is the human brain theoretically controllable? Second, which areas of the brain are most influential in constraining or facilitating changes in brain state trajectories? Further, I seek to examine the structural effect on the control actions through solving the optimal control problem under different boundary conditions. I quantify the efficiency of regions in terms of the energy cost for the brain state transition from the default mode to task modes. This analysis is extended to the perturbation analysis of trajectories and is applied to the comparison between the group with mild traumatic brain injury(mTBI) and the healthy group. My research is the first to demonstrate how control theory can be used to analyze human brain networks

Second CLIPS Conference Proceedings, volume 1

Topics covered at the 2nd CLIPS Conference held at the Johnson Space Center, September 23-25, 1991 are given. Topics include rule groupings, fault detection using expert systems, decision making using expert systems, knowledge representation, computer aided design and debugging expert systems

Real-Time Storytelling with Events in Virtual Worlds

We present an accessible interactive narrative tool for creating stories among a virtual populace inhabiting a fully-realized 3D virtual world. Our system supports two modalities: assisted authoring where a human storyteller designs stories using a storyboard-like interface called CANVAS, and exploratory authoring where a human author experiences a story as it happens in real-time and makes on-the-fly narrative trajectory changes using a tool called Storycraft. In both cases, our system analyzes the semantic content of the world and the narrative being composed, and provides automated assistance such as completing partially-specified stories with causally complete sequences of intermediate actions. At its core, our system revolves around events -â?? pre-authored multi-actor task sequences describing interactions between groups of actors and props. These events integrate complex animation and interaction tasks with precision control and expose them as atoms of narrative significance to the story direction systems. Events are an accessible tool and conceptual metaphor for assembling narrative arcs, providing a tightly-coupled solution to the problem of converting author intent to real-time animation synthesis. Our system allows simple and straightforward macro- and microscopic control over large numbers of virtual characters with diverse and sophisticated behavior capabilities, and reduces the complicated action space of an interactive narrative by providing analysis and user assistance in the form of semi-automation and recommendation services

Fourth Annual Workshop on Space Operations Applications and Research (SOAR 90)

The proceedings of the SOAR workshop are presented. The technical areas included are as follows: Automation and Robotics; Environmental Interactions; Human Factors; Intelligent Systems; and Life Sciences. NASA and Air Force programmatic overviews and panel sessions were also held in each technical area

The 1991 Goddard Conference on Space Applications of Artificial Intelligence

The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers in this proceeding fall into the following areas: Planning and scheduling, fault monitoring/diagnosis/recovery, machine vision, robotics, system development, information management, knowledge acquisition and representation, distributed systems, tools, neural networks, and miscellaneous applications