A fast dual algorithm for kernel logistic regression

This paper gives a new iterative algorithm for kernel logistic regression. It is based on the solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization algorithm for Support Vector Machines. Asymptotic convergence of the algorithm is proved. Computational experiments show that the algorithm is robust and fast. The algorithmic ideas can also be used to give a fast dual algorithm for solving the optimization problem arising in the inner loop of Gaussian Process classifiers.

On the bifurcation in a “modulated” logistic map

We comment on a paper by Luang [On the bifurcation in a ''modulated'' logistic map, Physics Letters A 194(1994) 57]. The numerical evidence given in that paper, for a peculiar type of bifurcation, is shown to be incorrect. The causes of such anomalous results are explained. An accurate bifurcation diagram for the map is also given.

Learning polyhedral classifiers using logistic function

In this paper we propose a new algorithm for learning polyhedral classifiers. In contrast to existing methods for learning polyhedral classifier which solve a constrained optimization problem, our method solves an unconstrained optimization problem. Our method is based on a logistic function based model for the posterior probability function. We propose an alternating optimization algorithm, namely, SPLA1 (Single Polyhedral Learning Algorithm1) which maximizes the loglikelihood of the training data to learn the parameters. We also extend our method to make it independent of any user specified parameter (e.g., number of hyperplanes required to form a polyhedral set) in SPLA2. We show the effectiveness of our approach with experiments on various synthetic and real world datasets and compare our approach with a standard decision tree method (OC1) and a constrained optimization based method for learning polyhedral sets.

Large-Scale Elastic Net Regularized Linear Classification SVMs and Logistic Regression

Elastic Net Regularizers have shown much promise in designing sparse classifiers for linear classification. In this work, we propose an alternating optimization approach to solve the dual problems of elastic net regularized linear classification Support Vector Machines (SVMs) and logistic regression (LR). One of the sub-problems turns out to be a simple projection. The other sub-problem can be solved using dual coordinate descent methods developed for non-sparse L2-regularized linear SVMs and LR, without altering their iteration complexity and convergence properties. Experiments on very large datasets indicate that the proposed dual coordinate descent - projection (DCD-P) methods are fast and achieve comparable generalization performance after the first pass through the data, with extremely sparse models.

Accelerating Information Diffusion in Social Networks Under the Susceptible-Infected-Susceptible Epidemic Model

Standard Susceptible-Infected-Susceptible (SIS) epidemic models assume that a message spreads from the infected to the susceptible nodes due to only susceptible-infected epidemic contact. We modify the standard SIS epidemic model to include direct recruitment of susceptible individuals to the infected class at a constant rate (independent of epidemic contacts), to accelerate information spreading in a social network. Such recruitment can be carried out by placing advertisements in the media. We provide a closed form analytical solution for system evolution in the proposed model and use it to study campaigning in two different scenarios. In the first, the net cost function is a linear combination of the reward due to extent of information diffusion and the cost due to application of control. In the second, the campaign budget is fixed. Results reveal the effectiveness of the proposed system in accelerating and improving the extent of information diffusion. Our work is useful for devising effective strategies for product marketing and political/social-awareness/crowd-funding campaigns that target individuals in a social network.

EEG seizure in bilateral ECT is different between low and high stimulus doses

Melancholic depressive patients referred for ECT were randomized to receive either low dose (n = 20) or high dose (n = 20) stimulus applied bifrontotemporally. The two stimulus groups were comparable on the clinical variables. The EEG seizure was recorded on two channels (right and left frontal), digitized, coded and analyzed offline without knowledge of ECT parameters. EEG seizure was of comparable duration in the two stimulus (high dose and low dose) groups. A new composite measure, Strength-Symmetry-Index (SSI), based on strength and symmetry of seizure EEG was computed using fractal geometry. The SSI of the early-seizure was higher in the high dose than in the low dose ECT group. In a stepwise, logistic regression model, this variable contributed to 65% with correct classification of high dose and low dose ECT seizures.

Critical lattice size limit for synchronized chaotic state in one- and two-dimensional diffusively coupled map lattices

We consider diffusively coupled map lattices with P neighbors (where P is arbitrary) and study the stability of the synchronized state. We show that there exists a critical lattice size beyond which the synchronized state is unstable. This generalizes earlier results for nearest neighbor coupling. We confirm the analytical results by performing numerical simulations on coupled map lattices with logistic map at each node. The above analysis is also extended to two-dimensional P-neighbor diffusively coupled map lattices.

Japanese encephalitis: pathogenesis, prophylactics and therapeutics

Japanese encephalitis (JE) is one of the most dreaded mosquito-borne viral encephalitis known to afflict humans. The Japanese encephalitis virus (JEV) is a neurotropic flavivirus that affects the CNS, causing extensive damage that may lead to fatality in about one third of bpatients. Half of the survivors suffer from severe neuropshychiatric sequelae. With nearly 3 billion people living under the current JE-endemic region, recurring incidents of epidemic are being reported at regular intervals. With no established antiviral therapies against JE available, vaccination has been the only way of preventing JE. Two types of JE vaccines are currently in vogue although the safety of administering them is questionable, in certain individuals. Thus, there is a need to develop a safe, affordable and potent JE vaccine and this review addresses the current efforts in this direction. This review also focuses on the pathophysiology of JE and efforts towards a possible breakthrough in anti-JEV therapy.

Estimating Frequencies of Minority Nevirapine-Resistant Strains in Chronically HIV-1-Infected Individuals Naive to Nevirapine by Using Stochastic Simulations and a Mathematical Model

Nevirapine forms the mainstay of our efforts to curtail the pediatric AIDS epidemic through prevention of mother-to-child transmission of HIV-1. A key limitation, however, is the rapid selection of HIV-1 strains resistant to nevirapine following the administration of a single dose. This rapid selection of resistance suggests that nevirapine-resistant strains preexist in HIV-1 patients and may adversely affect outcomes of treatment. The frequencies of nevirapine-resistant strains in vivo, however, remain poorly estimated, possibly because they exist as a minority below current assay detection limits. Here, we employ stochastic simulations and a mathematical model to estimate the frequencies of strains carrying different combinations of the common nevirapine resistance mutations K103N, V106A, Y181C, Y188C, and G190A in chronically infected HIV-1 patients naive to nevirapine. We estimate the relative fitness of mutant strains from an independent analysis of previous competitive growth assays. We predict that single mutants are likely to preexist in patients at frequencies (similar to 0.01% to 0.001%) near or below current assay detection limits (>0.01%), emphasizing the need for more-sensitive assays. The existence of double mutants is subject to large stochastic variations. Triple and higher mutants are predicted not to exist. Our estimates are robust to variations in the recombination rate, cellular superinfection frequency, and the effective population size. Thus, with 10(7) to 10(8) infected cells in HIV-1 patients, even when undetected, nevirapine-resistant genomes may exist in substantial numbers and compromise efforts to prevent mother-to-child transmission of HIV-1, accelerate the failure of subsequent antiretroviral treatments, and facilitate the transmission of drug resistance.

The PE and PPE proteins of Mycobacterium tuberculosis

India already has earned the dubious distinction of being one of the countries with the highest incidence of tuberculosis (TB). The conventional control measures have had little impact on the relentless march of the TB epidemic. Potential solutions to this problem include the development of new drugs and an effective TB vaccine. In this perspective, identification of the mycobacterial components that have important role(s) in the establishment of the infection assumes crucial importance. Mycobacterium tuberculosis is an intracellular pathogen and it resides inside the macrophage, which is considered to be the most important component of the immune system. M. tuberculosis possesses two highly polymorphic sets of genes called the PE and PPE families. These unique families of proteins account for about 10% of the mycobacterial genome and have drawn considerable interest from different schools of M. tuberculosis researchers across the globe. In this review, we discuss the importance of these proteins in the regulation of dendritic cell and macrophage immune-effector functions, as well as the relevance of these proteins in the clinical manifestation of TB. This information may be helpful to better understand the immunological importance of PE/PPE proteins and their roles in mycobacterial virulence. (C) 2011 Elsevier Ltd. All rights reserved.

Climate change induced risk in water quality control problems

A modeling framework is presented in this paper, integrating hydrologic scenarios projected from a General Circulation Model (GCM) with a water quality simulation model to quantify the future expected risk. Statistical downscaling with a Canonical Correlation Analysis (CCA) is carried out to develop the future scenarios of hydro-climate variables starting with simulations provided by a GCM. A Multiple Logistic Regression (MLR) is used to quantify the risk of Low Water Quality (LWQ) corresponding to a threshold quality level, by considering the streamflow and water temperature as explanatory variables. An Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) presented in an earlier study is then used to develop adaptive policies to address the projected water quality risks. Application of the proposed methodology is demonstrated with the case study of Tunga-Bhadra river in India. The results showed that the projected changes in the hydro-climate variables tend to diminish DO levels, thus increasing the future risk levels of LWQ. (C) 2012 Elsevier B.V. All rights reserved.

Molecular Epidemiology of HIV-1 Subtypes in India: Origin and Evolutionary History of the Predominant Subtype C

Background: India has the third largest HIV-1 epidemic with 2.4 million infected individuals. Molecular epidemiological analysis has identified the predominance of HIV-1 subtype C (HIV-1C). However, the previous reports have been limited by sample size, and uneven geographical distribution. The introduction of HIV-1C in India remains uncertain due to this lack of structured studies. To fill the gap, we characterised the distribution pattern of HIV-1 subtypes in India based on data collection from nationwide clinical cohorts between 2007 and 2011. We also reconstructed the time to the most recent common ancestor (tMRCA) of the predominant HIV-1C strains. Methodology/Principal Findings: Blood samples were collected from 168 HIV-1 seropositive subjects from 7 different states. HIV-1 subtypes were determined using two or three genes, gag, pol, and env using several methods. Bayesian coalescent-based approach was used to reconstruct the time of introduction and population growth patterns of the Indian HIV-1C. For the first time, a high prevalence (10%) of unique recombinant forms (BC and A1C) was observed when two or three genes were used instead of one gene (p<0.01; p = 0.02, respectively). The tMRCA of Indian HIV-1C was estimated using the three viral genes, ranged from 1967 (gag) to 1974 (env). Pol-gene analysis was considered to provide the most reliable estimate 1971, (95% CI: 1965-1976)]. The population growth pattern revealed an initial slow growth phase in the mid-1970s, an exponential phase through the 1980s, and a stationary phase since the early 1990s. Conclusions/Significance: The Indian HIV-1C epidemic originated around 40 years ago from a single or few genetically related African lineages, and since then largely evolved independently. The effective population size in the country has been broadly stable since the 1990s. The evolving viral epidemic, as indicated by the increase of recombinant strains, warrants a need for continued molecular surveillance to guide efficient disease intervention strategies.

Co-evolution of content popularity and delivery in mobile P2P networks

Mobile P2P technology provides a scalable approach for content delivery to a large number of users on their mobile devices. In this work, we study the dissemination of a single item of content (e. g., an item of news, a song or a video clip) among a population of mobile nodes. Each node in the population is either a destination (interested in the content) or a potential relay (not yet interested in the content). There is an interest evolution process by which nodes not yet interested in the content (i.e., relays) can become interested (i.e., become destinations) on learning about the popularity of the content (i.e., the number of already interested nodes). In our work, the interest in the content evolves under the linear threshold model. The content is copied between nodes when they make random contact. For this we employ a controlled epidemic spread model. We model the joint evolution of the copying process and the interest evolution process, and derive joint fluid limit ordinary differential equations. We then study the selection of parameters under the content provider's control, for the optimization of various objective functions that aim at maximizing content popularity and efficient content delivery.

Optimal forwarding in delay tolerant networks with multiple destinations

We study the trade-off between delivery delay and energy consumption in a delay tolerant network in which a message (or a file) has to be delivered to each of several destinations by epidemic relaying. In addition to the destinations, there are several other nodes in the network that can assist in relaying the message. We first assume that, at every instant, all the nodes know the number of relays carrying the packet and the number of destinations that have received the packet. We formulate the problem as a controlled continuous time Markov chain and derive the optimal closed loop control (i.e., forwarding policy). However, in practice, the intermittent connectivity in the network implies that the nodes may not have the required perfect knowledge of the system state. To address this issue, we obtain an ODE (i.e., fluid) approximation for the optimally controlled Markov chain. This fluid approximation also yields an asymptotically optimal open loop policy. Finally, we evaluate the performance of the deterministic policy over finite networks. Numerical results show that this policy performs close to the optimal closed loop policy.

Non-polio enteroviruses and their association with acute diarrhea in children in India

A causative agent in approximately 40% of diarrhea] cases. still remains unidentified. Though many enteroviruses (EVs) are transmitted through fecal-oral route and replicate in the intestinal cells, their association with acute diarrhea has not so far been recognized due to lack of detailed epidemiological investigations. This long-term, detailed molecular epidemiological study aims to conclusively determine the association of non-polio enteroviruses (NPEVs) with acute diarrhea in comaparison with rotavirus (RV) in children. Diarrheal stool specimens from 2161 children aged 0-2 years and 169 children between 2 and 9 years, and 1800 normal stool samples from age-matched healthy children between 0 and 9 years were examined during 2008-2012 for enterovirus (oral polio vaccine strains (OPVs) and NPEVs). Enterovirus serotypes were identified by complete VP1 gene sequence analysis. Enterovirus and rotavirus were detected in 19.01% (380/2330) and 13.82% (322/2330) diarrheal stools. During the study period, annual prevalence of EV- and RV-associated diarrhea ranged between 8% and 22%, but with contrasting seasonal prevalence with RV predominating during winter months and NPEV prevailing in other seasons. NPEVs are associated with epidemics-like outbreaks during which they are detected in up to 50% of diarrheic children, and in non-epidemic seasons in 0-10% of the patients. After subtraction of OPV-positive diarrheal cases (1.81%), while NPEVs are associated with about 17% of acute diarrhea, about 6% of healthy children showed asymptomatic NPEV excretion. Of 37 NPEV serotypes detected in diarrheal children, seven echovirus types 1, 7, 11, 13, 14, 30 and 33 are frequently observed, with Ell being more prevalent followed by E30. In conclusion, NPEVs are significantly associated with acute diarrhea, and NPEVs and rotavirus exhibit contrasting seasonal predominance. This study signifies the need for a new direction of research on enteroviruses involving systematic analysis of their contribution to diarrheal burden. (C) 2013 Elsevier B.V. All rights reserved.