This paper concerns the problem of matrix completion, which is to estimate a matrix from observations in a small subset of indices. We propose a calibrated spectrum elastic net method with a sum of the nuclear and Frobenius penalties and develop an iterative algorithm to solve the convex minimization problem. The iterative algorithm alternates between imputing the missing entries in the incomplete matrix by the current guess and estimating the matrix by a scaled soft-thresholding singular value decomposition of the imputed matrix until the resulting matrix converges. A calibration step follows to correct the bias caused by the Frobenius penalty. Under proper coherence conditions and for suitable penalties levels, we prove that the proposed estimator achieves an error bound of nearly optimal order and in proportion to the noise level. This provides a unified analysis of the noisy and noiseless matrix completion problems. Simulation results are presented to compare our proposal with previous ones.

LAGE is a systematic framework developed in Java. The motivation of LAGE is to provide a scalable and parallel solution to reconstruct Gene Regulatory Networks (GRNs) from continuous gene expression data for very large amount of genes. The basic idea of our framework is motivated by the philosophy of divideand-conquer. Specifically, LAGE recursively partitions genes into multiple overlapping communities with much smaller sizes, learns intra-community GRNs respectively before merge them altogether. Besides, the complete information of overlapping communities serves as the byproduct, which could be used to mine meaningful functional modules in biological networks.

The improvement of medical care quality is a significant interest for the future years. The fight against nosocomial infections (NI) in the intensive care units (ICU) is a good example. We will focus on a set of observations which reflect the dynamic aspect of the decision, result of the application of a Medical Decision Support System (MDSS). This system has to make dynamic decision on temporal data. We use dynamic Bayesian network (DBN) to model this dynamic process. It is a temporal reasoning within a real-time environment; we are interested in the Dynamic Decision Support Systems in healthcare domain (MDDSS).

In this paper secured wireless communication using fuzzy logic based high speed public key cryptography (FLHSPKC) has been proposed by satisfying the major issues likes computational safety, power management and restricted usage of memory in wireless communication. Wireless Sensor Network (WSN) has several major constraints likes inadequate source of energy, restricted computational potentiality and limited memory. Though conventional Elliptic Curve Cryptography (ECC) which is a sort of public key cryptography used in wireless communication provides equivalent level of security like other existing public key algorithm using smaller parameters than other but this traditional ECC does not take care of all these major limitations in WSN. In conventional ECC consider Elliptic curve point p, an arbitrary integer k and modulus m, ECC carry out scalar multiplication kP mod m, which takes about 80% of key computation time on WSN. In this paper proposed FLHSPKC scheme provides some novel strategy including novel soft computing based strategy to speed up scalar multiplication in conventional ECC and which in turn takes shorter computational time and also satisfies power consumption restraint, limited usage of memory without hampering the security level. Performance analysis of the different strategies under FLHSPKC scheme and comparison study with existing conventional ECC methods has been done.

Trust is a fundamental concept in many real-world applications such as e-commerce and peer-to-peer networks. In these applications, users can generate local opinions about the counterparts based on direct experiences, and these opinions can then be aggregated to build trust among unknown users. The mechanism to build new trust relationships based on existing ones is referred to as trust inference. State-of-the-art trust inference approaches employ the transitivity property of trust by propagating trust along connected users. In this paper, we propose a novel trust inference model (Ma-Trust) by exploring an equally important property of trust, i.e., the multi-aspect property. MaTrust directly characterizes multiple latent factors for each trustor and trustee from the locally-generated trust relationships. Furthermore, it can naturally incorporate prior knowledge as specified factors. These factors in turn serve as the basis to infer the unseen trustworthiness scores. Experimental evaluations on real data sets show that the proposed MaTrust significantly outperforms several benchmark trust inference models in both effectiveness and efficiency.

In this paper, we present CrowdSense, an algorithm for estimating the crowd’s majority opinion by querying only a subset of it. CrowdSense works in an online fashion where examples come one at a time and it dynamically samples subsets of labelers based on an exploration/exploitation criterion. The algorithm produces a weighted combination of a subset of the labelers’ votes that approximates the crowd’s opinion. We also present two probabilistic variants of CrowdSense that are based on different assumptions on the joint probability distribution between the labelers’ votes and the majority vote. Our experiments demonstrate that we can reliably approximate the entire crowd’s vote by collecting opinions from a representative subset of the crowd.

A large number of transients visit big cities on any given day and they visit crowded areas and come in contact with many people. However, epidemiological studies have not paid much attention to the role of this subpopulation in disease spread. In the present work, we extend a synthetic population model of Washington DC metro area to include leisure and business travelers. This approach involves combining Census data, activity surveys, and geospatial data to build a detailed minute-by-minute simulation of population interaction. We simulate a flu-like disease outbreak both with and without the transient population to evaluate the effect of the transients on outbreak size and peak day in terms of number of residents infected. Results show that there are significantly more infections when transients are considered. We also evaluate interventions like closing big museums and encouraging use of hand sanitizers at those musuems. Surprisingly closing musuems does not result in a significant difference in the epidemic. However, we find that if the use of hand sanitizer reduces the infectivity and suceptibility to 80% or 60% of the original values, it is as effective as closing museums for a few days or entirely eliminating the effect of transients. If infectivity and susceptibility are reduced to 40% or 20%, it reduces the number of resident infections over the period of 120 days by 10% and 13%.

Statistical models almost always yield predictions that are more accurate than those of human experts. However, humans are better at data acquisition and at recognizing atypical circumstances. We use prediction markets to combine predictions from groups of humans and artificial-intelligence agents and show that they are more robust than those from groups of humans or agents alone.

We study the computational complexity of optimal bribery and manipulation schemes for sports tournaments with uncertain information: cup; challenge or caterpillar; and round robin. Our results carry over to the equivalent voting rules: sequential pair-wise elections, cup, and Copeland, when the set of candidates is exactly the set of voters. This restriction creates new difficulties for most existing algorithms. The complexity of bribery and manipulation are well studied, almost always assuming deterministic information about votes and results. We assume that for candidates i and j the probability that i beats j and the costs of lowering each probability by fixed increments are known to the manipulators. We provide complexity analyses for cup, challenge, and round robin competitions ranging from polynomial time to np^pp. This shows that the introduction of uncertainty into the reasoning process drastically increases the complexity of bribery problems in some instances.

The ever-growing literature in biomedicine makes it virtually impossible for individuals to grasp all the information relevant to their interests. Since even experts' knowledge is likely to be incomplete, important associations among key biomedical concepts may remain unnoticed in the flood of information. Discovering those implicit, hidden knowledge is called hypothesis discovery. This paper reports our preliminary work on hypothesis discovery, which takes advantage of a syllogistic chain of relations extracted from existing knowledge (i.e., published literature). We consider such chains of relations as implicit patterns or rules to generate potential hypotheses. The generated hypotheses are then ranked according to their plausibility judged from the reliability of the rule which generated the hypothesis and the analogical resemblance between new and existing knowledge. We discuss the validity of the proposed approach on the entire Medline database.