1,720,971 research outputs found
Generalised temporal network inference
No full textNetwork inference is becoming increasingly central in the analysis of complex phenomena as it allows to obtain understandable models of entities interactions. Among the many possible graphical models, Markov Random Fields are widely used as they are strictly connected to a probability distribution assumption that allow to model a variety of different data. The inference of such models can be guided by two priors: sparsity and non-stationarity. In other words, only few connections are necessary to explain the phenomenon under observation and, as the phenomenon evolves, the underlying connections that explain it may change accordingly. This thesis contains two general methods for the inference of temporal graphical models that deeply rely on the concept of temporal consistency, i.e., the underlying structure of the system is similar (i.e., consistent) in time points that model the same behaviour (i.e., are dependent). The first contribution is a model that allows to be flexible in terms of probability assumption, temporal consistency, and dependency. The second contribution studies the previously introduces model in the presence of Gaussian partially un-observed data. Indeed, it is necessary to explicitly tackle the presence of un-observed data in order to avoid introducing misrepresentations in the inferred graphical model. All extensions are coupled with fast and non-trivial minimisation algorithms that are extensively validate on synthetic and real-world data. Such algorithms and experiments are implemented in a large and well-designed Python library that comprehends many tools for the modelling of multivariate data. Lastly, all the presented models have many hyper-parameters that need to be tuned on data. On this regard, we analyse different model selection strategies showing that a stability-based approach performs best in presence of multi-networks and multiple hyper-parameters
Missing Values in Multiple Joint Inference of Gaussian Graphical Models
No full textReal-world phenomena are often not fully measured or completely observable, raising the so-called
missing data problem. As a consequence, the need of developing ad-hoc techniques that cope
with such issue arises in many inference contexts. In this paper, we focus on the inference of
Gaussian Graphical Models (GGMs) from multiple input datasets having complex relationships
(e.g. multi-class or temporal). We propose a method that generalises state-of-the-art approaches
to the inference of both multi-class and temporal GGMs while naturally dealing with two types
of missing data: partial and latent. Synthetic experiments show that our performance is better
than state-of-the-art. In particular, we compared results with single network inference methods
that suitably deal with missing data, and multiple joint network inference methods coupled with
standard pre-processing techniques (e.g. imputing). When dealing with fully observed datasets
our method analytically reduces to state-of-the-art approaches providing a good alternative as our
implementation reaches convergence in shorter or comparable time. Finally, we show that properly
addressing the missing data problem in a multi-class real-world example, allows us to discover
interesting varying patterns
Cancer Mutational Signatures Identification with Sparse Dictionary Learning
No full textSomatic DNA mutations are a characteristic of cancerous cells, being usually key in the origin and development of cancer. In the last few years, somatic mutations have been studied in order to understand which processes or conditions may generate them, with the purpose of developing prevention and treatment strategies. In this work we propose a novel sparse regularised method that aims at extracting mutational signatures from somatic mutations. We developed a pipeline that extracts the dataset from raw data and performs the analysis returning the signatures and their relative usage frequencies. A thorough comparison between our method and the state of the art procedure reveals that our pipeline can be used alternatively without losing information and possibly gaining more interpretability and precision
Latent Variable Time-varying Network Inference
No full textIn many applications of finance, biology and sociology, complex systems involve entities interacting with each other. These processes have the peculiarity of evolving over time and of comprising latent factors, which influence the system without being explicitly measured. In this work we present latent variable time-varying graphical lasso (LTGL), a method for multivariate time-series graphical modelling that considers the influence of hidden or unmeasurable factors. The estimation of the contribution of the latent factors is embedded in the model which produces both sparse and low-rank components for each time point. In particular, the first component represents the connectivity structure of observable variables of the system, while the second represents the influence of hidden factors, assumed to be few with respect to the observed variables. Our model includes temporal consistency on both components, providing an accurate evolutionary pattern of the system. We derive a tractable optimisation algorithm based on alternating direction method of multipliers, and develop a scalable and efficient implementation which exploits proximity operators in closed form. LTGL is extensively validated on synthetic data, achieving optimal performance in terms of accuracy, structure learning and scalability with respect to ground truth and state-of-the-art methods for graphical inference. We conclude with the application of LTGL to real case studies, from biology and finance, to illustrate how our method can be successfully employed to gain insights on multivariate time-series data
Hey there's DALILA: a DictionAry LearnIng LibrAry
Full text linkDictionary Learning and Representation Learning are machine learning methods for decomposition, denoising and reconstruction of data with a wide range of applications such as text recognition, image processing and biological processes understanding. In this work we present DALILA, a scientific Python library for regularised dictionary learning and regularised representation learning that allows to impose prior knowledge, if available. DALILA, differently from the others available libraries for this purpose, is flexible and modular. DALILA is designed to be easily extended for custom needs. Moreover, it is compliant with the most widespread ML Python library and this allows for a straightforward usage and integration.
We here present and discuss the theoretical aspects and discuss its strength points and implementation
Switching from Glargine to Degludec is not associated with an overt change in glucose control in a cohort of patients with type 1 diabetes: a CGM analysis
No full textA Robust Method for Statistical Testing of Empirical Power-Law Distributions
No full textThe World-Wide-Web is a complex system naturally represented by a directed network of documents (nodes) connected through hyperlinks (edges). In this work, we focus on one of the most relevant topological properties that characterize the network, i.e. being scale-free. A directed network is scale-free if its in-degree and out-degree distributions have an approximate and asymptotic power-law behavior. If we consider the Web as a whole, it presents empirical evidence of such property.
On the other hand, when we restrict the study of the degree distributions to specific sub-categories of websites, there is no longer strong evidence for it. For this reason, many works questioned the almost universal ubiquity of the scale-free property. Moreover, existing statistical methods to test whether an empirical degree distribution follows a power law suffer when dealing with large sample size and/or noisy data. In this paper, we propose an extension of a state-of-the-art method that overcomes such problems by applying a subsampling procedure on the graphs performing Random Walks (RW). We show on synthetic experiments that even small variations of true power-law distributed data causes the state-of-the-art method to reject the hypothesis, while the proposed method is more sound and stable under such variations. Lastly, we perform a study on 3 websites showing that indeed, depending on the sub-categories of website we consider, some accept and some refuse the hypothesis of being power-law. We argue that our method could be used to further explore sub-categories of websites in order to better characterize their topological properties deriving from different generative principles: central or peripheral
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
- …
