1,720,960 research outputs found
Mathematical Modelling in Biosciences and Discrete Optimization
Full text linkThis thesis explores innovative methodologies in two distinct areas of Applied
Mathematics: Mathematical Modelling in Biosciences and Discrete Optimization.
The work is structured into two parts, each addressing critical
challenges in its respective field while offering practical applications and advancements.
The first part focuses on Mathematical Modelling in Biosciences, presented
in two chapters. The first chapter introduces a statistical monitoring
approach using funnel plots for the early detection of COVID-19 Variants of
Concern (VoCs). This methodology demonstrates remarkable utility in epidemiological
surveillance by providing a simple, cost-effective, and real-time
tool for identifying anomalous patterns in regional reproduction numbers. Its
practical impact lies in its ability to complement genomic sequencing efforts
by enabling more targeted and efficient investigations, ultimately supporting
timely public health interventions.
The second chapter reconstructs the temporal profile of new COVID-19
cases in Italy during the first wave of 2020, addressing the significant underreporting
that hindered accurate epidemiological assessments. By leveraging
dynamic system identification and regularized inverse problem-solving techniques,
this work not only offers a quantitative correction for underreported
data but also provides a robust framework for evaluating the impact of nonpharmaceutical
interventions.
The second part focuses on Discrete Optimization and consists of two
chapters. The first chapter addresses the design and optimization of fewbit
neural networks tailored for classification problems under few-shot learning
scenarios. A novel voting structure is proposed to extend the framework
to multi-class classification, offering practical applications in scenarios where
computational efficiency and adaptability are paramount.
The second chapter investigates the unassigned Distance Geometry Problem
in the Manhattan norm, applied to the Mobile Positioning Problem in
grid-like geometries. This formulation is particularly suited to scenarios such
as mobile device positioning in urban environments, where the assignment of
distances between devices is unknown.This thesis explores innovative methodologies in two distinct areas of Applied
Mathematics: Mathematical Modelling in Biosciences and Discrete Optimization.
The work is structured into two parts, each addressing critical
challenges in its respective field while offering practical applications and advancements.
The first part focuses on Mathematical Modelling in Biosciences, presented
in two chapters. The first chapter introduces a statistical monitoring
approach using funnel plots for the early detection of COVID-19 Variants of
Concern (VoCs). This methodology demonstrates remarkable utility in epidemiological
surveillance by providing a simple, cost-effective, and real-time
tool for identifying anomalous patterns in regional reproduction numbers. Its
practical impact lies in its ability to complement genomic sequencing efforts
by enabling more targeted and efficient investigations, ultimately supporting
timely public health interventions.
The second chapter reconstructs the temporal profile of new COVID-19
cases in Italy during the first wave of 2020, addressing the significant underreporting
that hindered accurate epidemiological assessments. By leveraging
dynamic system identification and regularized inverse problem-solving techniques,
this work not only offers a quantitative correction for underreported
data but also provides a robust framework for evaluating the impact of nonpharmaceutical
interventions.
The second part focuses on Discrete Optimization and consists of two
chapters. The first chapter addresses the design and optimization of fewbit
neural networks tailored for classification problems under few-shot learning
scenarios. A novel voting structure is proposed to extend the framework
to multi-class classification, offering practical applications in scenarios where
computational efficiency and adaptability are paramount.
The second chapter investigates the unassigned Distance Geometry Problem
in the Manhattan norm, applied to the Mobile Positioning Problem in
grid-like geometries. This formulation is particularly suited to scenarios such
as mobile device positioning in urban environments, where the assignment of
distances between devices is unknown
Correction of Italian under-reporting in the first COVID-19 wave via age-specific deconvolution of hospital admissions.
No full textWhen the COVID-19 pandemic first emerged in early 2020, healthcare and bureaucratic systems worldwide were caught off guard and largely unprepared to deal with the scale and severity of the outbreak. In Italy, this led to a severe underreporting of infections during the first wave of the spread. The lack of accurate data is critical as it hampers the retrospective assessment of nonpharmacological interventions, the comparison with the following waves, and the estimation and validation of epidemiological models. In particular, during the first wave, reported cases of new infections were strikingly low if compared with their effects in terms of deaths, hospitalizations and intensive care admissions. In this paper, we observe that the hospital admissions during the second wave were very well explained by the convolution of the reported daily infections with an exponential kernel. By formulating the estimation of the actual infections during the first wave as an inverse problem, its solution by a regularization approach is proposed and validated. In this way, it was possible to compute corrected time series of daily infections for each age class. The new estimates are consistent with the serological survey published in June 2020 by the National Institute of Statistics (ISTAT) and can be used to speculate on the total number of infections occurring in Italy during 2020, which appears to be about double the number officially recorded
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
Multiobjective linear ensembles for robust and sparse training of few-bit neural networks
No full textTraining neural networks (NNs) using combinatorial optimization solvers has gained attention in recent years. In low-data settings, the use of state-of-the-art mixed integer linear programming solvers, for instance, has the potential to exactly train an NN while avoiding computing-intensive training and hyperparameter tuning and simultaneously training and sparsifying the network. We study the case of few-bit discrete-valued neural networks, both binarized neural networks (BNNs) whose values are restricted to +/- 1 and integer-valued neural networks (INNs) whose values lie in the range {-P,& mldr;,P}. Few-bit NNs receive increasing recognition because of their lightweight architecture and ability to run on low-power devices: for example, being implemented using Boolean operations. This paper proposes new methods to improve the training of BNNs and INNs. Our contribution is a multiobjective ensemble approach based on training a single NN for each possible pair of classes and applying a majority voting scheme to predict the final output. Our approach results in the training of robust sparsified networks whose output is not affected by small perturbations on the input and whose number of active weights is as small as possible. We empirically compare this BeMi approach with the current state of the art in solver-based NN training and with traditional gradient-based training, focusing on BNN learning in few-shot contexts. We compare the benefits and drawbacks of INNs versus BNNs, bringing new light to the distribution of weights over the {-P,& mldr;,P} interval. Finally, we compare multiobjective versus single-objective training of INNs, showing that robustness and network simplicity can be acquired simultaneously, thus obtaining better test performances. Although the previous state-of-the-art approaches achieve an average accuracy of 51.1% on the Modified National Institute of Standards and Technology data set, the BeMi ensemble approach achieves an average accuracy of 68.4% when trained with 10 images per class and 81.8% when trained with 40 images per class while having up to 75.3% NN links removed
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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