Incentive Compatible Influence Maximization in Social Networks with Application to Viral Marketing

Information diffusion and influence maximization are important and extensively studied problems in social networks. Various models and algorithms have been proposed in the literature in the context of the influence maximization problem. A crucial assumption in all these studies is that the influence probabilities are known to the social planner. This assumption is unrealistic since the influence probabilities are usually private information of the individual agents and strategic agents may not reveal them truthfully. Moreover, the influence probabilities could vary significantly with the type of the information flowing in the network and the time at which the information is propagating in the network. In this paper, we use a mechanism design approach to elicit influence probabilities truthfully from the agents. Our main contribution is to design a scoring rule based mechanism in the context of the influencer-influencee model. In particular, we show the incentive compatibility of the mechanisms and propose a reverse weighted scoring rule based mechanism as an appropriate mechanism to use.

Synthesis, characterization and thermal degradation of dual temperature- and pH-sensitive RAFT-made copolymers of N,N-(dimethylamino)ethyl methacrylate and methyl methacrylate

Poly{(N,N-(dimethylamino)ethyl methacrylate]-co-(methyl methacrylate)} copolymers of various compositions were synthesized by reversible addition-fragmentation chain transfer (RAFT) polymerization at 70 degrees C in N,N-dimethylformamide. The polymer molecular weights and molecular weight distributions were obtained from size exclusion chromatography, and they indicated the controlled nature of the RAFT polymerizations; the polydispersity indices are in the range 1.11.3. The reactivity ratios of N,N-(dimethylamino)ethyl methacrylate (DMAEMA) and methyl methacrylate (MMA) (rDMAEMA = 0.925 and rMMA = 0.854) were computed by the extended KelenTudos method at high conversions, using compositions obtained from 1H NMR. The pH- and temperature-sensitive behaviour were studied in aqueous solution to confirm dual responsiveness of these copolymers. The thermal properties of the copolymers with various compositions were investigated by differential scanning calorimetry and thermogravimetric analysis. The kinetics of thermal degradation were determined by Friedmann and Chang techniques to evaluate various parameters such as the activation energy, the order and the frequency factor. (c) 2012 Society of Chemical Industry

Low complexity adaptation for channel shortening equalizers

Since the days of Digital Subscriber Links(DSL), Time Domain Equalizers(TEQ's) have been used to combat time dispersive channels in Multicarrier Systems. In this paper, we propose computationally inexpensive techniques to recompute TEQ weights in the presence of changes in the channel, especially over fast fading channels. The techniques use no extra information except the perturbation itself, and provide excellent approximations to the new TEQ weights. The proposed adaptation techniques are shown to perform admirably well for small changes in channels for OFDM systems.

A weight based context analysis system to provide a required UMM service

Ubiquitous Computing is an emerging paradigm which facilitates user to access preferred services, wherever they are, whenever they want, and the way they need, with zero administration. While moving from one place to another the user does not need to specify and configure their surrounding environment, the system initiates necessary adaptation by itself to cope up with the changing environment. In this paper we propose a system to provide context-aware ubiquitous multimedia services, without user’s intervention. We analyze the context of the user based on weights, identify the UMMS (Ubiquitous Multimedia Service) based on the collected context information and user profile, search for the optimal server to provide the required service, then adapts the service according to user’s local environment and preferences, etc. The experiment conducted several times with different context parameters, their weights and various preferences for a user. The results are quite encouraging.

Comparative genomics reveals `novel' Fur regulated sRNAs and coding genes in diverse proteobacteria

Ferric uptake regulator (Fur) is a transcriptional regulator controlling the expression of genes involved in iron homeostasis and plays an important role in pathogenesis. Fur-regulated sRNAs/CDSs were found to have upstream Fur Binding Sites (FBS). We have constructed a Positional Weight Matrix from 100 known FBS (19 nt) and tracked the `Orphan' FBSs. Possible Fur regulated sRNAs and CDSs were identified by comparing their genomic locations with the `Orphan' FBSs identified. Thirty-eight `novel' and all known Fur regulated sRNAs in nine proteobacteria were identified. In addition, we identified high scoring FBSs in the promoter regions of the 304 CDSs and 68 of them were involved in siderophore biosynthesis, iron-transporters, two-component system, starch/sugar metabolism, sulphur/methane metabolism, etc. The present study shows that the Fur regulator controls the expression of genes involved in diverse metabolic activities and it is not limited to iron metabolism alone. (C) 2012 Elsevier B.V. All rights reserved.

Fully complex-valued ELM classifiers for human action recognition

In this paper, we present a fast learning neural network classifier for human action recognition. The proposed classifier is a fully complex-valued neural network with a single hidden layer. The neurons in the hidden layer employ the fully complex-valued hyperbolic secant as an activation function. The parameters of the hidden layer are chosen randomly and the output weights are estimated analytically as a minimum norm least square solution to a set of linear equations. The fast leaning fully complex-valued neural classifier is used for recognizing human actions accurately. Optical flow-based features extracted from the video sequences are utilized to recognize 10 different human actions. The feature vectors are computationally simple first order statistics of the optical flow vectors, obtained from coarse to fine rectangular patches centered around the object. The results indicate the superior performance of the complex-valued neural classifier for action recognition. The superior performance of the complex neural network for action recognition stems from the fact that motion, by nature, consists of two components, one along each of the axes.

Method for Seismic Microzonation with Geotechnical Aspects

This study presents an overview of seismic microzonation and existing methodologies with a newly proposed methodology covering all aspects. Earlier seismic microzonation methods focused on parameters that affect the structure or foundation related problems. But seismic microzonation has generally been recognized as an important component of urban planning and disaster management. So seismic microzonation should evaluate all possible hazards due to earthquake and represent the same by spatial distribution. This paper presents a new methodology for seismic microzonation which has been generated based on location of study area and possible associated hazards. This new method consists of seven important steps with defined output for each step and these steps are linked with each other. Addressing one step and respective result may not be seismic microzonation, which is practiced widely. This paper also presents importance of geotechnical aspects in seismic microzonation and how geotechnical aspects affect the final map. For the case study, seismic hazard values at rock level are estimated considering the seismotectonic parameters of the region using deterministic and probabilistic seismic hazard analysis. Surface level hazard values are estimated considering site specific study and local site effects based on site classification/characterization. The liquefaction hazard is estimated using standard penetration test data. These hazard parameters are integrated in Geographical Information System (GIS) using Analytic Hierarchy Process (AHP) and used to estimate hazard index. Hazard index is arrived by following a multi-criteria evaluation technique - AHP, in which each theme and features have been assigned weights and then ranked respectively according to a consensus opinion about their relative significance to the seismic hazard. The hazard values are integrated through spatial union to obtain the deterministic microzonation map and probabilistic microzonation map for a specific return period. Seismological parameters are widely used for microzonation rather than geotechnical parameters. But studies show that the hazard index values are based on site specific geotechnical parameters.

An adaptation framework for head-pose classification in dynamic multi-view scenarios

Multi-view head-pose estimation in low-resolution, dynamic scenes is difficult due to blurred facial appearance and perspective changes as targets move around freely in the environment. Under these conditions, acquiring sufficient training examples to learn the dynamic relationship between position, face appearance and head-pose can be very expensive. Instead, a transfer learning approach is proposed in this work. Upon learning a weighted-distance function from many examples where the target position is fixed, we adapt these weights to the scenario where target positions are varying. The adaptation framework incorporates reliability of the different face regions for pose estimation under positional variation, by transforming the target appearance to a canonical appearance corresponding to a reference scene location. Experimental results confirm effectiveness of the proposed approach, which outperforms state-of-the-art by 9.5% under relevant conditions. To aid further research on this topic, we also make DPOSE- a dynamic, multi-view head-pose dataset with ground-truth publicly available with this paper.

Feature weighting for clustering by particle swarm optimization

Clustering has been the most popular method for data exploration. Clustering is partitioning the data set into sub-partitions based on some measures say the distance measure, each partition has its own significant information. There are a number of algorithms explored for this purpose, one such algorithm is the Particle Swarm Optimization(PSO) which is a population based heuristic search technique derived from swarm intelligence. In this paper we present an improved version of the Particle Swarm Optimization where, each feature of the data set is given significance accordingly by adding some random weights, which also minimizes the distortions in the dataset if any. The performance of the above proposed algorithm is evaluated using some benchmark datasets from Machine Learning Repository. The experimental results shows that our proposed methodology performs significantly better than the previously performed experiments.

Decode-and-forward relay beamforming for secrecy with finite-alphabet input

In this letter, we compute the secrecy rate of decode-and-forward (DF) relay beamforming with finite input alphabet of size M. Source and relays operate under a total power constraint. First, we observe that the secrecy rate with finite-alphabet input can go to zero as the total power increases, when we use the source power and the relay weights obtained assuming Gaussian input. This is because the capacity of an eavesdropper can approach the finite-alphabet capacity of 1/2 log(2) M with increasing total power, due to the inability to completely null in the direction of the eavesdropper. We then propose a transmit power control scheme where the optimum source power and relay weights are obtained by carrying out transmit power (source power plus relay power) control on DF with Gaussian input using semi-definite programming, and then obtaining the corresponding source power and relay weights which maximize the secrecy rate for DF with finite-alphabet input. The proposed power control scheme is shown to achieve increasing secrecy rates with increasing total power with a saturation behavior at high total powers.

Decode-and-forward relay beamforming for secrecy with imperfect CSI and multiple eavesdroppers

In this paper, we evaluate secrecy rates in cooperative relay beamforming in the presence of imperfect channel state information (CSI) and multiple eavesdroppers. A source-destination pair aided by.. out of.. relays, 1 <= k <= M, using decode-and-forward relay beamforming is considered. We compute the worst case secrecy rate with imperfect CSI in the presence of multiple eavesdroppers, where the number of eavesdroppers can be more than the number of relays. We solve the optimization problem for all possible relay combinations to find the secrecy rate and optimum source and relay weights subject to a total power constraint. We relax the rank-1 constraint on the complex semi-definite relay weight matrix and use S-procedure to reformulate the optimization problem that can be solved using convex semi-definite programming.

Prioritisation of micro-catchments based on morphology

Two multicriterion decision-making methods, namely `compromise programming' and the `technique for order preference by similarity to an ideal solution' are employed to prioritise 22 micro-catchments (A1 to A22) of Kherthal catchment, Rajasthan, India and comparative analysis is performed using the compound parameter approach. Seven criteria - drainage density, bifurcation ratio, stream frequency, form factor, elongation ratio, circulatory ratio and texture ratio - are chosen for the evaluation. The entropy method is employed to estimate weights or relative importance of the criterion which ultimately affects the ranking pattern or prioritisation of micro-catchments. Spearman rank correlation coefficients are estimated to measure the extent to which the ranks obtained are correlated. Based on the average ranking approach supported by sensitivity analysis, micro-catchments A6, A10, A3 are preferred (owing to their low ranking) for further improvements with suitable conservation and management practices, and other micro-catchments can be processed accordingly at a later phase on a priority basis. It is concluded that the present approach can be explored for other similar situations with appropriate modifications.

Experimental Studies on the Mechanics of Cohesive Frictional Granular Media

The mechanical behaviour of cohesive-frictional granular materials is a combination of the strength pervading as intergranular friction (represented as an angle of internal friction - Phi), and the cohesion (C) between these particles. Most behavioral or constitutive models of this class of granular materials comprise of a cohesion and frictional component with no regard to the length scale i.e. from the micro structural models through the continuum models. An experimental study has been made on a model granular material, viz. angular sand with different weights of binding agents (varying degrees of cohesion) at multiple length scales to physically map this phenomenon. Cylindrical specimen of various diameters - 10, 20, 38, 100, 150 mm (and with an aspect ratio of 2) are reconstituted with 2, 4 and 8% by weight of a binding agent. The magnitude of this cohesion is analyzed using uniaxial compression tests and it is assumed to correspond to the peak in the normalized stress-strain plot. Increase in the cohesive strength of the material is seen with increasing size of the specimen. A possibility of ``entanglement'' occurring in larger specimens is proposed as a possible reason for deviation from a continuum framework.

Extension of TSVM to multi-class and hierarchical text classification problems With general losses

Transductive SVM (TSVM) is a well known semi-supervised large margin learning method for binary text classification. In this paper we extend this method to multi-class and hierarchical classification problems. We point out that the determination of labels of unlabeled examples with fixed classifier weights is a linear programming problem. We devise an efficient technique for solving it. The method is applicable to general loss functions. We demonstrate the value of the new method using large margin loss on a number of multi-class and hierarchical classification datasets. For maxent loss we show empirically that our method is better than expectation regularization/constraint and posterior regularization methods, and competitive with the version of entropy regularization method which uses label constraints.

Anomalous phase transitions in soft colloid-polymer binary mixtures

We have shown earlier [1] that these PGNPs resemble star polymers or spherical brushes in terms of their morphology in the melt. However, these particles show dynamics in melt which is quite different from other soft colloidal particles. Since most of the work on soft colloidal particles have been performed in solutions we have now explored the phase behavior of the PGNPs in good solvent using microscopic structural and dynamical measurements on binary mixtures of homopolymers and soft colloids consisting of polymer grafted nanoparticles. We observe anomalous structural and dynamical phase transitions of these binary mixtures, including appearance of spontaneous orientational alignment and logarithmic structural relaxations, as a function of added homopolymers of different molecular weights. Our experiments points to the possibility of exploiting the phase space in density and homopolymer size, of such hybrid systems, to create new materials with unique properties.