6368187
Algorithms for derivation of structurally stable Hamiltonian signed graphs
A graph Sp,q,n refers to a signed graph with p nodes and q edges with n being the number of negative edges. We introduce two theorems to facilitate identification of the complete set of balanced signed graph configurations for any p-node Hamiltonian signed graph in terms of p, q and n. This allows for the development of computational procedures to efficiently determine the structural stability of a signed graph. This is potentially useful for the planning and analysis of complex situations or scenarios which can be depicted as signed graphs. Through the application of the theorems, the state of balance of a signed graph structure or its affinity towards balance can be determined in a more time-efficient manner compared to any explicit enumeration algorithm. E-mail: pg0212208t@ntu.edu.sg E-mail: fnh@crl.nmsu.edu E-mail: dwunsch@ece.umr.edu
12571939
Inflight Rerouting for an Unmanned Aerial Vehicle
The availability of new information can often necessitate rerouting of a vehicle. In (1), the set of nonoverlapping useful vicinities of the reconnaissance sites, S, and the 3D space over them was represented using a discrete graph-model. Here, we consider two cases in which an unmanned reconnaissance aerial vehicle must be rerouted. The first scenario involves not visiting useful vicinities, S t ⊂ S, due to newly reported unacceptable threat- levels in their neighborhood. In the second scenario, visitations to useful vicinities, S r ⊂ S, is made redundant due to decreased importance of the sites to which they offer a vantage observation-point. Our earlier graph-model (1) is general enough to provide a basic framework for consideration of rerouting in both cases. We give an algorithm to obtain a graph-model, from the earlier one, that allows efficient gene encoding and efficient application of GA operators to obtain new alternative rerouting plans.
17611569
Solution to the Fixed Airbase Problem for Autonomous URAV Site Visitation Sequencing
In [1], we modeled the problem of safe site visitation sequencing for an unmanned reconnaissance aerial vehicle (URAV) in graph-theoretic language. Our goal was to uncover several alternative flight paths along which the URAV can fly safely, no more than once, to all sites of interest. We offered a solution to a simpler abstraction of this problem using GAs – if we could not uncover any Hamiltonian cycles we reported maximally long paths between any two vertices. This work reports two orthogonal extensions of [1] – a. In addition to possibly reporting Hamiltonian cycles, the GA reports several alternative long paths that begin at the airbase. b. A randomized version of the gene migration scheme is used. The randomization does not appear to affect the quality of the best plan. However it raises the average quality of the best k plans. We give a modified gene encoding scheme and report simulation results.
5921169
Learning to rank networked entities
Several algorithms have been proposed to learn to rank entities modeled as feature vectors, based on relevance feedback. However, these algorithms do not model network connections or relations between entities. Meanwhile, Pagerank and variants find the stationary distribution of a reasonable but arbitrary Markov walk over a network, but do not learn from relevance feedback. We present a framework for ranking networked entities based on Markov walks with parameterized conductance values associated with the network edges. We propose two flavors of conductance learning problems in our framework. In the first setting, relevance feedback comparing node-pairs hints that the user has one or more hidden preferred communities with large edge conductance, and the algorithm must discover these communities. We present a constrained maximum entropy network flow formulation whose dual can be solved efficiently using a cutting-plane approach and a quasi-Newton optimizer. In the second setting, edges have types, and relevance feedback hints that each edge type has a potentially different conductance, but this is fixed across the whole network. Our algorithm learns the conductances using an approximate Newton method.
34654890
An efficient recovery procedure for fault tolerance in distributed systems
Abstract In this article, the problem of fault tolerance in real-time distributed systems is addressed by providing an efficient algorithmic procedure for recovery in such systems. Our recovery procedure does not require the application of an intrusive checkpointing procedure, but uses contextual information exchanged between the distributed system components during normal system progress. On detection of a failure, each process, through an efficient propagation mechanism, will have locally computed a recovery point that is mutually consistent with the recovery points computed by other processes. During recovery, each process considers the contextual information recorded after its established recovery point; a minimal amount of computations are redone. Also, a minimal number of processes are required to roll back, and the time needed to revert to normal operation is found to be minimal, thus contributing to the real-time requirements for such systems. Furthermore, the stable storage requirement needed to implement our recovery procedure is bounded and minimal. Proofs of correctness of our procedure are provided, in particular, the absence of orphan messages, message losses, and duplications is shown. An example illustrating our recovery procedure is also provided.
7563707
Learning to track 3D human motion from silhouettes
We describe a sparse Bayesian regression method for recovering 3D human body motion directly from silhouettes extracted from monocular video sequences. No detailed body shape model is needed, and realism is ensured by training on real human motion capture data. The tracker estimates 3D body pose by using Relevance Vector Machine regression to combine a learned autoregressive dynamical model with robust shape descriptors extracted automatically from image silhouettes. We studied several different combination methods, the most effective being to learn a nonlinear observation-update correction based on joint regression with respect to the predicted state and the observations. We demonstrate the method on a 54-parameter full body pose model, both quantitatively using motion capture based test sequences, and qualitatively on a test video sequence.
13564962
Modelling and verification of interworking between SIP and H.323
There are currently two standards for signaling and control of Internet telephone calls, namely ITU-T Recommendation H.323 and the IETF session initiation protocol (SIP). Both protocols provide similar functionality using different mechanisms. Both H.323 and SIP provide interoperability, flexibility and scalability in different aspects and environments. Interworking between SIP and H.323 is essential to ensure full end-to-end connectivity through the network, although the current number of deployed commercial SIP endpoints is relatively small compared to widely deployed H.323 endpoints.To formally specify the SIP-H.323 Interworking, this paper gives a new system model for interworking between SIP and H.323. Five main components of this system are modeled using system description language and message sequence chart: H.323 endpoint, H.323 gatekeeper, SIP-H.323 Interworking facility, SIP server, SIP endpoint. We design and define the internal structure and behavior for each component, which is established for simulating and verifying interworking between SIP and H.323. Under two different configurations that both protocols work within the same administrative domain and that both protocols are operating in separate administrative domains, we use a series of scenarios to show that our model meets the functional specifications outlined in the SIP-H323-Interworking specification documents.
39910433
Augmented neural networks for task scheduling
Abstract We propose a new approach, called Augmented Neural Networks (AugNN) for solving the task-scheduling problem. This approach is a hybrid of the heuristic and the neural networks approaches. While retaining all the advantages of the heuristic approach, AugNN incorporates learning, to find improved solutions iteratively. This new framework maps the problem structure to a neural network and utilizes domain specific knowledge for finding solutions. The problem we address is that of minimizing the makespan in scheduling n tasks on m machines where the tasks follow a precedence relation and task pre-emption is not allowed. Solutions obtained from AugNN using various learning rules are compared with six different commonly used heuristics. AugNN approach provides significant improvements over heuristic results. In just a few iterations, the gap between the lower bound and the obtained solution is reduced by as much as 58% for some heuristics, without any increase in computational complexity. While the heuristics found solutions in the range of 5.8–26.9% of the lower bound, on average, AugNN found solutions in the range of 3.3–11.1%, a significant improvement.