Modelling and analysis of new coolstreaming for P2P IPTV

Peer to peer networks are being used extensively nowadays for file sharing, video on demand and live streaming. For IPTV, delay deadlines are more stringent compared to file sharing. Coolstreaming was the first P2P IPTV system. In this paper, we model New Coolstreaming (newer version of Coolstreaming) via a queueing network. We use two time scale decomposition of Markov chains to compute the stationary distribution of number of peers and the expected number of substreams in the overlay which are not being received at the required rate due to parent overloading. We also characterize the end-to-end delay encountered by a video packet received by a user and originated at the server. Three factors contribute towards the delay. The first factor is the mean shortest path length between any two overlay peers in terms of overlay hops of the partnership graph which is shown to be O (log n) where n is the number of peers in the overlay. The second factor is the mean number of routers between any two overlay neighbours which is seen to be at most O (log N-I) where N-I is the number of routers in the internet. Third factor is the mean delay at a router in the internet. We provide an approximation of this mean delay E W]. Thus, the mean end to end delay in New Coolstreaming is shown to be upper bounded by O (log E N]) (log N-I) E (W)] where E N] is the mean number of peers at a channel.

Classification, representation, and automatic extraction of deformation features in sheet metal parts

This paper presents classification, representation and extraction of deformation features in sheet-metal parts. The thickness is constant for these shape features and hence these are also referred to as constant thickness features. The deformation feature is represented as a set of faces with a characteristic arrangement among the faces. Deformation of the base-sheet or forming of material creates Bends and Walls with respect to a base-sheet or a reference plane. These are referred to as Basic Deformation Features (BDFs). Compound deformation features having two or more BDFs are defined as characteristic combinations of Bends and Walls and represented as a graph called Basic Deformation Features Graph (BDFG). The graph, therefore, represents a compound deformation feature uniquely. The characteristic arrangement of the faces and type of bends belonging to the feature decide the type and nature of the deformation feature. Algorithms have been developed to extract and identify deformation features from a CAD model of sheet-metal parts. The proposed algorithm does not require folding and unfolding of the part as intermediate steps to recognize deformation features. Representations of typical features are illustrated and results of extracting these deformation features from typical sheet metal parts are presented and discussed. (C) 2013 Elsevier Ltd. All rights reserved.

Product Dimension of Forests and Bounded Treewidth Graphs

The product dimension of a graph G is defined as the minimum natural number l such that G is an induced subgraph of a direct product of l complete graphs. In this paper we study the product dimension of forests, bounded treewidth graphs and k-degenerate graphs. We show that every forest on n vertices has product dimension at most 1.441 log n + 3. This improves the best known upper bound of 3 log n for the same due to Poljak and Pultr. The technique used in arriving at the above bound is extended and combined with a well-known result on the existence of orthogonal Latin squares to show that every graph on n vertices with treewidth at most t has product dimension at most (t + 2) (log n + 1). We also show that every k-degenerate graph on n vertices has product dimension at most inverted right perpendicular5.545 k log ninverted left perpendicular + 1. This improves the upper bound of 32 k log n for the same by Eaton and Rodl.

Scalable flow-sensitive pointer analysis for fava with strong updates

The ability to perform strong updates is the main contributor to the precision of flow-sensitive pointer analysis algorithms. Traditional flow-sensitive pointer analyses cannot strongly update pointers residing in the heap. This is a severe restriction for Java programs. In this paper, we propose a new flow-sensitive pointer analysis algorithm for Java that can perform strong updates on heap-based pointers effectively. Instead of points-to graphs, we represent our points-to information as maps from access paths to sets of abstract objects. We have implemented our analysis and run it on several large Java benchmarks. The results show considerable improvement in precision over the points-to graph based flow-insensitive and flow-sensitive analyses, with reasonable running time.

Sufficient conditions for formation of a network topology by self-interested agents

Networks such as organizational network of a global company play an important role in a variety of knowledge management and information diffusion tasks. The nodes in these networks correspond to individuals who are self-interested. The topology of these networks often plays a crucial role in deciding the ease and speed with which certain tasks can be accomplished using these networks. Consequently, growing a stable network having a certain topology is of interest. Motivated by this, we study the following important problem: given a certain desired network topology, under what conditions would best response (link addition/deletion) strategies played by self-interested agents lead to formation of a pairwise stable network with only that topology. We study this interesting reverse engineering problem by proposing a natural model of recursive network formation. In this model, nodes enter the network sequentially and the utility of a node captures principal determinants of network formation, namely (1) benefits from immediate neighbors, (2) costs of maintaining links with immediate neighbors, (3) benefits from indirect neighbors, (4) bridging benefits, and (5) network entry fee. Based on this model, we analyze relevant network topologies such as star graph, complete graph, bipartite Turan graph, and multiple stars with interconnected centers, and derive a set of sufficient conditions under which these topologies emerge as pairwise stable networks. We also study the social welfare properties of the above topologies.

The Lovasz θ function, SVMs and finding large dense subgraphs

The Lovasz θ function of a graph, is a fundamental tool in combinatorial optimization and approximation algorithms. Computing θ involves solving a SDP and is extremely expensive even for moderately sized graphs. In this paper we establish that the Lovasz θ function is equivalent to a kernel learning problem related to one class SVM. This interesting connection opens up many opportunities bridging graph theoretic algorithms and machine learning. We show that there exist graphs, which we call SVM−θ graphs, on which the Lovasz θ function can be approximated well by a one-class SVM. This leads to a novel use of SVM techniques to solve algorithmic problems in large graphs e.g. identifying a planted clique of size Θ(n√) in a random graph G(n,12). A classic approach for this problem involves computing the θ function, however it is not scalable due to SDP computation. We show that the random graph with a planted clique is an example of SVM−θ graph, and as a consequence a SVM based approach easily identifies the clique in large graphs and is competitive with the state-of-the-art. Further, we introduce the notion of a ''common orthogonal labeling'' which extends the notion of a ''orthogonal labelling of a single graph (used in defining the θ function) to multiple graphs. The problem of finding the optimal common orthogonal labelling is cast as a Multiple Kernel Learning problem and is used to identify a large common dense region in multiple graphs. The proposed algorithm achieves an order of magnitude scalability compared to the state of the art.

Generalized distributive law for ML decoding of STBCs: further results

The problem of designing good Space-Time Block Codes (STBCs) with low maximum-likelihood (ML) decoding complexity has gathered much attention in the literature. All the known low ML decoding complexity techniques utilize the same approach of exploiting either the multigroup decodable or the fast-decodable (conditionally multigroup decodable) structure of a code. We refer to this well known technique of decoding STBCs as Conditional ML (CML) decoding. In [1], we introduced a framework to construct ML decoders for STBCs based on the Generalized Distributive Law (GDL) and the Factor-graph based Sum-Product Algorithm, and showed that for two specific families of STBCs, the Toepltiz codes and the Overlapped Alamouti Codes (OACs), the GDL based ML decoders have strictly less complexity than the CML decoders. In this paper, we introduce a `traceback' step to the GDL decoding algorithm of STBCs, which enables roughly 4 times reduction in the complexity of the GDL decoders proposed in [1]. Utilizing this complexity reduction from `traceback', we then show that for any STBC (not just the Toeplitz and Overlapped Alamouti Codes), the GDL decoding complexity is strictly less than the CML decoding complexity. For instance, for any STBC obtained from Cyclic Division Algebras that is not multigroup or conditionally multigroup decodable, the GDL decoder provides approximately 12 times reduction in complexity compared to the CML decoder. Similarly, for the Golden code, which is conditionally multigroup decodable, the GDL decoder is only about half as complex as the CML decoder.

Fault-tolerant secret key generation

Mobile nodes observing correlated data communicate using an insecure bidirectional switch to generate a secret key, which must remain concealed from the switch. We are interested in fault-tolerant secret key rates, i.e., the rates of secret key generated even if a subset of nodes drop out before the completion of the communication protocol. We formulate a new notion of fault-tolerant secret key capacity, and present an upper bound on it. This upper bound is shown to be tight when the random variables corresponding to the observations of nodes are exchangeable. Further, it is shown that one round of interaction achieves the fault-tolerant secret key capacity in this case. The upper bound is also tight for the case of a pairwise independent network model consisting of a complete graph, and can be attained by a noninteractive protocol.

On interference alignment for symmetrically connected interference networks with line of sight channels at finite powers

In this work, interference alignment for a class of Gaussian interference networks with general message demands, having line of sight (LOS) channels, at finite powers is considered. We assume that each transmitter has one independent message to be transmitted and the propagation delays are uniformly distributed between 0 and (L - 1) (L >; 0). If receiver-j, j ∈{1,2,..., J}, requires the message of transmitter-i, i ∈ {1, 2, ..., K}, we say (i, j) belongs to a connection. A class of interference networks called the symmetrically connected interference network is defined as a network where, the number of connections required at each transmitter-i is equal to ct for all i and the number of connections required at each receiver-j is equal to cr for all j, for some fixed positive integers ct and cr. For such networks with a LOS channel between every transmitter and every receiver, we show that an expected sum-spectral efficiency (in bits/sec/Hz) of at least K/(e+c1-1)(ct+1) (ct/ct+1)ct log2 (1+min(i, j)∈c|hi, j|2 P/WN0) can be achieved as the number of transmitters and receivers tend to infinity, i.e., K, J →∞ where, C denotes the set of all connections, hij is the channel gain between transmitter-i and receiver-j, P is the average power constraint at each transmitter, W is the bandwidth and N0 W is the variance of Gaussian noise at each receiver. This means that, for an LOS symmetrically connected interference network, at any finite power, the total spectral efficiency can grow linearly with K as K, J →∞. This is achieved by extending the time domain interference alignment scheme proposed by Grokop et al. for the k-user Gaussian interference channel to interference networks.

Detecting symmetry in scalar fields using augmented extremum graphs

Visualizing symmetric patterns in the data often helps the domain scientists make important observations and gain insights about the underlying experiment. Detecting symmetry in scalar fields is a nascent area of research and existing methods that detect symmetry are either not robust in the presence of noise or computationally costly. We propose a data structure called the augmented extremum graph and use it to design a novel symmetry detection method based on robust estimation of distances. The augmented extremum graph captures both topological and geometric information of the scalar field and enables robust and computationally efficient detection of symmetry. We apply the proposed method to detect symmetries in cryo-electron microscopy datasets and the experiments demonstrate that the algorithm is capable of detecting symmetry even in the presence of significant noise. We describe novel applications that use the detected symmetry to enhance visualization of scalar field data and facilitate their exploration.

A distributed TDMA slot scheduling algorithm for spatially correlated contention in WSNs

In wireless sensor networks (WSNs) the communication traffic is often time and space correlated, where multiple nodes in a proximity start transmitting at the same time. Such a situation is known as spatially correlated contention. The random access methods to resolve such contention suffers from high collision rate, whereas the traditional distributed TDMA scheduling techniques primarily try to improve the network capacity by reducing the schedule length. Usually, the situation of spatially correlated contention persists only for a short duration and therefore generating an optimal or sub-optimal schedule is not very useful. On the other hand, if the algorithm takes very large time to schedule, it will not only introduce additional delay in the data transfer but also consume more energy. To efficiently handle the spatially correlated contention in WSNs, we present a distributed TDMA slot scheduling algorithm, called DTSS algorithm. The DTSS algorithm is designed with the primary objective of reducing the time required to perform scheduling, while restricting the schedule length to maximum degree of interference graph. The algorithm uses randomized TDMA channel access as the mechanism to transmit protocol messages, which bounds the message delay and therefore reduces the time required to get a feasible schedule. The DTSS algorithm supports unicast, multicast and broadcast scheduling, simultaneously without any modification in the protocol. The protocol has been simulated using Castalia simulator to evaluate the run time performance. Simulation results show that our protocol is able to considerably reduce the time required to schedule.

STUDENT'S-t MIXTURE MODEL BASED MULTI-INSTRUMENT RECOGNITION IN POLYPHONIC MUSIC

We address the problem of multi-instrument recognition in polyphonic music signals. Individual instruments are modeled within a stochastic framework using Student's-t Mixture Models (tMMs). We impose a mixture of these instrument models on the polyphonic signal model. No a priori knowledge is assumed about the number of instruments in the polyphony. The mixture weights are estimated in a latent variable framework from the polyphonic data using an Expectation Maximization (EM) algorithm, derived for the proposed approach. The weights are shown to indicate instrument activity. The output of the algorithm is an Instrument Activity Graph (IAG), using which, it is possible to find out the instruments that are active at a given time. An average F-ratio of 0 : 7 5 is obtained for polyphonies containing 2-5 instruments, on a experimental test set of 8 instruments: clarinet, flute, guitar, harp, mandolin, piano, trombone and violin.

Temperature dependent electrical behaviour of Cu2SnS3 films

The temperature dependent electrical properties of the dropcasted Cu2SnS3 films have been measured in the temperature range 140 K to 317 K. The log I versus root V plot shows two regions. The region at lower bias is due to electrode limited Schottky emission and the higher bias region is due to bulk limited Poole Frenkel emission. The ideality factor is calculated from the ln I versus V plot for different temperatures fitted with the thermionic emission model and is found to vary from 6.05 eV to 12.23 eV. This large value is attributed to the presence of defects or amorphous layer at the Ag / Cu2SnS3 interface. From the Richardson's plot the Richardson's constant and the barrier height were calculated. Owing to the inhomogeneity in the barrier heights, the Richardson's constant and the barrier height were also calculated from the modified Richardson's plot. The I-V-T curves were also fitted using the thermionic field emission model. The barrier heights were found to be higher than those calculated using thermionic emission model. From the fit of the I-V-T curves to the field emission model, field emission was seen to dominate in the low temperature range of 140 K to 177 K. The temperature dependent current graphs show two regions of different mechanisms. The log I versus 1000/T plot gives activation energies E-a1 = 0.367095 - 0.257682 eV and E-a2 = 0.038416 - 0.042452 eV. The log ( I/T-2) versus 1000/T graph gives trap depths Phi(o1) = 0.314159 - 0.204752 eV and Phi(o2) = 0.007425- 0.011163 eV. With increasing voltage the activation energy E-a1 and the trap depth Phi(o1) decrease. From the ln (IT1/ 2) versus 1/T-1/ 4 graph, the low temperature region is due to variable range hopping mechanism and the high temperature region is due to thermionic emission. (C) 2014 Author(s).

A Dinuclear Cadmium(II) Schiff Base Thiocyanato Complex: Crystal Structure and Fluorescence

A new dinuclear cadmium(II) complex, Cd(L)(NCS)](2) (1) has been synthesized using a potentially tetradentate Schiff base ligand HL, 2-((E)-(2-(diethylamino) ethylimino) methyl)-6-methoxyphenol, obtained by the condensation of 2-diethylaminoethylamine and o-vanillin, and characterized by different physicochemical techniques. Crystal structure of the title complex was unambiguously established by single crystal X-ray diffraction which reveals that metal centers are connected by bridging phenolato and chelating methoxy oxygen atoms of the coordinating Schiff bases and embedded in severely distorted octahedral geometries. Fluorescence properties of the ligand and its complex, studied at room temperature indicate that later may serve as strong fluorescent

Predicting gene ontology annotations of orphan GWAS genes using protein-protein interactions

Background: The number of genome-wide association studies (GWAS) has increased rapidly in the past couple of years, resulting in the identification of genes associated with different diseases. The next step in translating these findings into biomedically useful information is to find out the mechanism of the action of these genes. However, GWAS studies often implicate genes whose functions are currently unknown; for example, MYEOV, ANKLE1, TMEM45B and ORAOV1 are found to be associated with breast cancer, but their molecular function is unknown. Results: We carried out Bayesian inference of Gene Ontology (GO) term annotations of genes by employing the directed acyclic graph structure of GO and the network of protein-protein interactions (PPIs). The approach is designed based on the fact that two proteins that interact biophysically would be in physical proximity of each other, would possess complementary molecular function, and play role in related biological processes. Predicted GO terms were ranked according to their relative association scores and the approach was evaluated quantitatively by plotting the precision versus recall values and F-scores (the harmonic mean of precision and recall) versus varying thresholds. Precisions of similar to 58% and similar to 40% for localization and functions respectively of proteins were determined at a threshold of similar to 30 (top 30 GO terms in the ranked list). Comparison with function prediction based on semantic similarity among nodes in an ontology and incorporation of those similarities in a k nearest neighbor classifier confirmed that our results compared favorably. Conclusions: This approach was applied to predict the cellular component and molecular function GO terms of all human proteins that have interacting partners possessing at least one known GO annotation. The list of predictions is available at http://severus.dbmi.pitt.edu/engo/GOPRED.html. We present the algorithm, evaluations and the results of the computational predictions, especially for genes identified in GWAS studies to be associated with diseases, which are of translational interest.