Action Recognition from Motion Capture Data using Meta-cognitive RBF Network Classifier

Action recognition plays an important role in various applications, including smart homes and personal assistive robotics. In this paper, we propose an algorithm for recognizing human actions using motion capture action data. Motion capture data provides accurate three dimensional positions of joints which constitute the human skeleton. We model the movement of the skeletal joints temporally in order to classify the action. The skeleton in each frame of an action sequence is represented as a 129 dimensional vector, of which each component is a 31) angle made by each joint with a fixed point on the skeleton. Finally, the video is represented as a histogram over a codebook obtained from all action sequences. Along with this, the temporal variance of the skeletal joints is used as additional feature. The actions are classified using Meta-Cognitive Radial Basis Function Network (McRBFN) and its Projection Based Learning (PBL) algorithm. We achieve over 97% recognition accuracy on the widely used Berkeley Multimodal Human Action Database (MHAD).

Human action recognition in H.264/AVC compressed domain using meta-cognitive radial basis function network

In this paper, we propose a H.264/AVC compressed domain human action recognition system with projection based metacognitive learning classifier (PBL-McRBFN). The features are extracted from the quantization parameters and the motion vectors of the compressed video stream for a time window and used as input to the classifier. Since compressed domain analysis is done with noisy, sparse compression parameters, it is a huge challenge to achieve performance comparable to pixel domain analysis. On the positive side, compressed domain allows rapid analysis of videos compared to pixel level analysis. The classification results are analyzed for different values of Group of Pictures (GOP) parameter, time window including full videos. The functional relationship between the features and action labels are established using PBL-McRBFN with a cognitive and meta-cognitive component. The cognitive component is a radial basis function, while the meta-cognitive component employs self-regulation to achieve better performance in subject independent action recognition task. The proposed approach is faster and shows comparable performance with respect to the state-of-the-art pixel domain counterparts. It employs partial decoding, which rules out the complexity of full decoding, and minimizes computational load and memory usage. This results in reduced hardware utilization and increased speed of classification. The results are compared with two benchmark datasets and show more than 90% accuracy using the PBL-McRBFN. The performance for various GOP parameters and group of frames are obtained with twenty random trials and compared with other well-known classifiers in machine learning literature. (C) 2015 Elsevier B.V. All rights reserved.

Network integration for international mergers and Acquisitions

Business activities are increasingly taking place across geographical and ownership boundaries. Post-Merger & Acquisition Integration (PMI) processes are more challenging in network organisations due to the extra complexity and interdependency associated with networks. However, network integration issues are not well addressed in the traditional M&A literature or the network organisation literature. Based on ten in-depth case studies across key industry sectors, this research identifies the essential network integration issues for international M&As with a configuration concept, and demonstrates different network integration patterns according to M&A objectives for growth and efficiency. This paper extends the theoretical understanding of PMI for network organisations. It can also provide practical guidance for managers to assess the feasibility of an M&A transition or to go through the PMI process successfully. Copyright © 2010 Inderscience Enterprises Ltd.

The design of an agent for modelling supply chain network dynamics

A useful insight into managerial decision making can be found from simulation of business systems, but existing work on simulation of supply chain behaviour has largely considered non-competitive chains. Where competitive agents have been examined, they have generally had a simple structure and been used for fundamental examination of stability and equilibria rather than providing practical guidance to managers. In this paper, a new agent for the study of competitive supply chain network dynamics is proposed. The novel features of the agent include the ability to select between competing vendors, distribute orders preferentially among many customers, manage production and inventory, and determine price based on competitive behaviour. The structure of the agent is related to existing business models and sufficient details are provided to allow implementation. The agent is tested to demonstrate that it recreates the main results of the existing modelling and management literature on supply chain dynamics. A brief exploration of competitive dynamics is given to confirm that the proposed agent can respond to competition. The results demonstrate that overall profitability for a supply chain network is maximised when businesses operate collectively. It is possible for an individual business to achieve higher profits by adopting a more competitive stance, but the consequence of this is that the overall profitability of the network is reduced. The agent will be of use for a broad range of studies on the long-run effect of management decisions on their network of suppliers and customers.

Altruism in the (Social) Network

This paper explores the role of social integration on altruistic behavior. To this aim, we develop a two-stage experimental protocol based on the classic Dictator Game. In the first stage, we ask a group of 77 undergraduate students in Economics to elicit their social network; in the second stage, each of them has to unilaterally decide over the division of a fixed amount of money to be shared with another anonymous member in the group. Our experimental design allows to control for other variables known to be relevant for altruistic behavior: framing and friendship/acquaintance relations. Consistently with previous research, we find that subjects favor their friends and that framing enhances altruistic behavior. Once we control for these effects, social integration (measured by betweenness, a standard centrality measure in network theory) has a positive effect on giving: the larger social isolation within the group, the more likely it is the emergence of selfish behavior. These results suggest that information on the network structure in which subjects are embedded is crucial to account for their behavior.

Comparisons of static, quasi-static and dynamic 3D porous media scale network models for two-phase immiscible flow in porous media

A dynamic 3D pore-scale network model is formulated for investigating the effect of interfacial tension and oil-water viscosity during chemical flooding. The model takes into account both viscous and capillary forces in analyzing the impact of chemical properties on flow behavior or displacement configuration, while the static model with conventional invasion percolation algorithm incorporates the capillary pressure only. From comparisons of simulation results from these models. it indicates that the static pore scale network model can be used successfully when the capillary number is low. With the capillary increases due to the enhancement of water viscosity or decrease of interfacial tension, only the quasi-static and dynamic model can give insight into the displacement mechanisms.

Astrophysics and Distributed Research utilising Advanced Computing (DiRAC)

Supporting presentation slides to accompany the Janet network end to end performance initiative workshop

Research Data Management — Activities and Challenges Report on the Knowledge Exchange Workshop

Workshop Research Data Management – Activities and Challenges 14-15 November 2011, Bonn The Knowledge Exchange initiative organised a workshop to highlight current activities and challenges with respect to research data management in the Knowledge Exchange partner countries and beyond. The workshop brought together experts from data centres, libraries, computational centres, funding organisations, publishing services and other institutions in the field of research and higher education who are working to improve research data management and encourage effective reuse of research data. A considerable part of the programme was dedicated to sharing perspectives from these communities, leading to the development of a roadmap of practical actions for the Knowledge Exchange initiative, partner organisations and other stakeholders to progress over the next two years. On the first day, principal investigators and project managers from a great variety of recent projects shared their insights on objectives and methods for improving data management ranging from discipline-specific to more general approaches. A series of short presentations of selected projects was followed by an extensive poster session that functioned as a “trade fair” of current trends and activities in the field of research data management. Moreover, the poster session offered ample network opportunities for participants. The second day was dedicated to intensive group discussions looking at a number of data management challenges. First the most important findings from the "Surfboard for 'Riding the Wave'" report were presented. This included the state of the art on activities and challenges in the field of research data management. The subgroups will concentrate on the following key themes: funding, incentives, training and technical infrastructure. These discussions culminated in the identification of practical recommendations for future cooperation on practical as well as on strategic levels that should be taken forward by the KE partner organisations and beyond. These activities aim to improve the sustainability of services and infrastructures at both national and international levels.

The development of the South Carolina Coastal Information Network and portal site: Bringing training opportunities and educational resources to coastal community officials and the public

The South Carolina Coastal Information Network (SCCIN) emerged as a result of a number of coastal outreach institutions working in partnership to enhance coordination of the coastal community outreach efforts in South Carolina. This organized effort, led by the S.C. Sea Grant Consortium and its Extension Program, includes partners from federal and state agencies, regional government agencies, and private organizations seeking to coordinate and/or jointly deliver outreach programs that target coastal community constituents. The Network was officially formed in 2006 with the original intention of fostering intra-and inter- agency communication, coordination, and cooperation. Network partners include the S.C. Sea Grant Consortium, S.C. Department of Health and Environmental Control – Office of Ocean and Coastal Resource Management and Bureau of Water, S.C. Department of Natural Resources – ACE Basin National Estuarine Research Reserve, North Inlet-Winyah Bay National Estuarine Research Reserve, Clemson University Cooperative Extension Service and Carolina Clear, Berkeley-Charleston-Dorchester Council of Governments, Waccamaw Regional Council of Governments, Urban Land Institute of South Carolina, S.C. Department of Archives and History, the National Oceanic and Atmospheric Administration – Coastal Services Center and Hollings Marine Laboratory, Michaux Conservancy, Ashley-Cooper Stormwater Education Consortium, the Coastal Waccamaw Stormwater Education Consortium, the S.C. Chapter of the U.S. Green Building Council, and the Lowcountry Council of Governments. (PDF contains 3 pages)

Proteomic analysis of the Cdc48/Ubx network identifies a role for Ubx2 in the regulation of lipid biosynthesis

Cdc48/p97 is an essential, highly abundant hexameric member of the AAA (ATPase associated with various cellular activities) family. It has been linked to a variety of processes throughout the cell but it is best known for its role in the ubiquitin proteasome pathway. In this system it is believed that Cdc48 behaves as a segregase, transducing the chemical energy of ATP hydrolysis into mechanical force to separate ubiquitin-conjugated proteins from their tightly-bound partners.

Current models posit that Cdc48 is linked to its substrates through a variety of adaptor proteins, including a family of seven proteins (13 in humans) that contain a Cdc48-binding UBX domain. As such, due to the complexity of the network of adaptor proteins for which it serves as the hub, Cdc48/p97 has the potential to exert a profound influence on the ubiquitin proteasome pathway. However, the number of known substrates of Cdc48/p97 remains relatively small, and smaller still is the number of substrates that have been linked to a specific UBX domain protein. As such, the goal of this dissertation research has been to discover new substrates and better understand the functions of the Cdc48 network. With this objective in mind, we established a proteomic screen to assemble a catalog of candidate substrate/targets of the Ubx adaptor system.

Here we describe the implementation and optimization of a cutting-edge quantitative mass spectrometry method to measure relative changes in the Saccharomyces cerevisiae proteome. Utilizing this technology, and in order to better understand the breadth of function of Cdc48 and its adaptors, we then performed a global screen to identify accumulating ubiquitin conjugates in cdc48-3 and ubxΔ mutants. In this screen different ubx mutants exhibited reproducible patterns of conjugate accumulation that differed greatly from each other, pointing to various unexpected functional specializations of the individual Ubx proteins.

As validation of our mass spectrometry findings, we then examined in detail the endoplasmic-reticulum bound transcription factor Spt23, which we identified as a putative Ubx2 substrate. In these studies ubx2Δ cells were deficient in processing of Spt23 to its active p90 form, and in localizing p90 to the nucleus. Additionally, consistent with reduced processing of Spt23, ubx2Δ cells demonstrated a defect in expression of their target gene OLE1, a fatty acid desaturase. Overall, this work demonstrates the power of proteomics as a tool to identify new targets of various pathways and reveals Ubx2 as a key regulator lipid membrane biosynthesis.