158 research outputs found
Deciphering Network Community Structure by Surprise
The analysis of complex networks permeates all sciences, from biology to
sociology. A fundamental, unsolved problem is how to characterize the community
structure of a network. Here, using both standard and novel benchmarks, we show
that maximization of a simple global parameter, which we call Surprise (S),
leads to a very efficient characterization of the community structure of
complex synthetic networks. Particularly, S qualitatively outperforms the most
commonly used criterion to define communities, Newman and Girvan's modularity
(Q). Applying S maximization to real networks often provides natural,
well-supported partitions, but also sometimes counterintuitive solutions that
expose the limitations of our previous knowledge. These results indicate that
it is possible to define an effective global criterion for community structure
and open new routes for the understanding of complex networks.Comment: 7 pages, 5 figure
The bootstrap -A review
The bootstrap, extensively studied during the last decade, has become a powerful tool in different areas of Statistical Inference. In this work, we present the main ideas of bootstrap methodology in several contexts, citing the most relevant contributions and illustrating with examples and simulation studies some interesting aspects
Estimation of genetic diversity in viral populations from next generation sequencing data with extremely deep coverage
In this paper we propose a method and discuss its computational
implementation as an integrated tool for the analysis of viral genetic
diversity on data generated by high-throughput sequencing. Most methods for
viral diversity estimation proposed so far are intended to take benefit of the
longer reads produced by some NGS platforms in order to estimate a population
of haplotypes. Our goal here is to take advantage of distinct virtues of a
certain kind of NGS platform - the platform SOLiD (Life Technologies) is an
example - that has not received much attention due to the short length of its
reads, which renders haplotype estimation very difficult. However, this kind of
platform has a very low error rate and extremely deep coverage per site and our
method is designed to take advantage of these characteristics. We propose to
measure the populational genetic diversity through a family of multinomial
probability distributions indexed by the sites of the virus genome, each one
representing the populational distribution of the diversity per site. The
implementation of the method focuses on two main optimization strategies: a
read mapping/alignment procedure that aims at the recovery of the maximum
possible number of short-reads; the estimation of the multinomial parameters
through a Bayesian approach, which, unlike simple frequency counting, allows
one to take into account the prior information of the control population within
the inference of a posterior experimental condition and provides a natural way
to separate signal from noise, since it automatically furnishes Bayesian
confidence intervals. The methods described in this paper have been implemented
as an integrated tool called Tanden (Tool for Analysis of Diversity in Viral
Populations).Comment: 30 pages, 5 figures, 2 tables, Tanden is written in C# (Microsoft),
runs on the Windows operating system, and can be downloaded from:
http://tanden.url.p
Statistical atlas based registration and planning for ablating bone tumors in minimally invasive interventions
Bone tumor ablation has been a viable treatment in a minimally invasive way compared with surgical resections. In this paper, two key challenges in the computer-Assisted bone tumor ablation have been addressed: 1) establishing the spatial transformation of patient's tumor with respect to a global map of the patient using a minimum number of intra-operative images and 2) optimal treatment planning for large tumors. Statistical atlas is employed to construct the global reference map. The atlas is deformably registered to a pair of intra-operative fluoroscopy images, constructing a patient-specific model, in order to reduce the radiation exposure to the sensitive patients such as pregnant and infants. The optimal treatment planning system incorporates clinical constraints on ablations and trajectories using a multiple objective optimization, which obtains optimal trajectory planning and ablation coverage using integer programming. The proposed system is presented and validated by experiments. © 2012 IEEE.published_or_final_versio
Fairness in Recommendation: Foundations, Methods and Applications
As one of the most pervasive applications of machine learning, recommender
systems are playing an important role on assisting human decision making. The
satisfaction of users and the interests of platforms are closely related to the
quality of the generated recommendation results. However, as a highly
data-driven system, recommender system could be affected by data or algorithmic
bias and thus generate unfair results, which could weaken the reliance of the
systems. As a result, it is crucial to address the potential unfairness
problems in recommendation settings. Recently, there has been growing attention
on fairness considerations in recommender systems with more and more literature
on approaches to promote fairness in recommendation. However, the studies are
rather fragmented and lack a systematic organization, thus making it difficult
to penetrate for new researchers to the domain. This motivates us to provide a
systematic survey of existing works on fairness in recommendation. This survey
focuses on the foundations for fairness in recommendation literature. It first
presents a brief introduction about fairness in basic machine learning tasks
such as classification and ranking in order to provide a general overview of
fairness research, as well as introduce the more complex situations and
challenges that need to be considered when studying fairness in recommender
systems. After that, the survey will introduce fairness in recommendation with
a focus on the taxonomies of current fairness definitions, the typical
techniques for improving fairness, as well as the datasets for fairness studies
in recommendation. The survey also talks about the challenges and opportunities
in fairness research with the hope of promoting the fair recommendation
research area and beyond.Comment: Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST
Combination of direct methods and homotopy in numerical optimal control: application to the optimization of chemotherapy in cancer
We consider a state-constrained optimal control problem of a system of two
non-local partial-differential equations, which is an extension of the one
introduced in a previous work in mathematical oncology. The aim is to minimize
the tumor size through chemotherapy while avoiding the emergence of resistance
to the drugs. The numerical approach to solve the problem was the combination
of direct methods and continuation on discretization parameters, which happen
to be insufficient for the more complicated model, where diffusion is added to
account for mutations. In the present paper, we propose an approach relying on
changing the problem so that it can theoretically be solved thanks to a
Pontryagin Maximum Principle in infinite dimension. This provides an excellent
starting point for a much more reliable and efficient algorithm combining
direct methods and continuations. The global idea is new and can be thought of
as an alternative to other numerical optimal control techniques
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