29 research outputs found
When do improved covariance matrix estimators enhance portfolio optimization? An empirical comparative study of nine estimators
The use of improved covariance matrix estimators as an alternative to the
sample estimator is considered an important approach for enhancing portfolio
optimization. Here we empirically compare the performance of 9 improved
covariance estimation procedures by using daily returns of 90 highly
capitalized US stocks for the period 1997-2007. We find that the usefulness of
covariance matrix estimators strongly depends on the ratio between estimation
period T and number of stocks N, on the presence or absence of short selling,
and on the performance metric considered. When short selling is allowed,
several estimation methods achieve a realized risk that is significantly
smaller than the one obtained with the sample covariance method. This is
particularly true when T/N is close to one. Moreover many estimators reduce the
fraction of negative portfolio weights, while little improvement is achieved in
the degree of diversification. On the contrary when short selling is not
allowed and T>N, the considered methods are unable to outperform the sample
covariance in terms of realized risk but can give much more diversified
portfolios than the one obtained with the sample covariance. When T<N the use
of the sample covariance matrix and of the pseudoinverse gives portfolios with
very poor performance.Comment: 30 page
Multi-Time-Scale Features for Accurate Respiratory Sound Classification
The COVID-19 pandemic has amplified the urgency of the developments in computer-assisted medicine and, in particular, the need for automated tools supporting the clinical diagnosis and assessment of respiratory symptoms. This need was already clear to the scientific community, which launched an international challenge in 2017 at the International Conference on Biomedical Health Informatics (ICBHI) for the implementation of accurate algorithms for the classification of respiratory sound. In this work, we present a framework for respiratory sound classification based on two different kinds of features: (i) short-term features which summarize sound properties on a time scale of tenths of a second and (ii) long-term features which assess sounds properties on a time scale of seconds. Using the publicly available dataset provided by ICBHI, we cross-validated the classification performance of a neural network model over 6895 respiratory cycles and 126 subjects. The proposed model reached an accuracy of 85%±3% and an precision of 80%±8%, which compare well with the body of literature. The robustness of the predictions was assessed by comparison with state-of-the-art machine learning tools, such as the support vector machine, Random Forest and deep neural networks. The model presented here is therefore suitable for large-scale applications and for adoption in clinical practice. Finally, an interesting observation is that both short-term and long-term features are necessary for accurate classification, which could be the subject of future studies related to its clinical interpretation
Territorial Development as an Innovation Driver: A Complex Network Approach
Rankings are a well-established tool to evaluate the performance of actors in different sectors of the economy, and their use is increasing even in the context of the startup ecosystem, both on a regional and on a global scale. Although rankings meet the demand for measurability and comparability, they often provide an oversimplified picture of the status quo, which, in particular, overlooks the variability of the socio-economic conditions in which the quantified results are achieved. In this paper, we describe an approach based on constructing a network of world countries, in which links are determined by mutual similarity in terms of development indicators. Through the instrument of community detection, we perform an unsupervised partition of the considered set of countries, aimed at interpreting their performance in the StartupBlink rankings. We consider both the global ranking and the specific ones (quality, quantity, business). After verifying if community membership is predictive of the success of a country in the considered ranking, we rate country performances in terms of the expectation based on community peers. We are thus able to identify cases in which performance is better than expected, providing a benchmark for countries in similar conditions, and cases in which performance is below the expectation, highlighting the need to strengthen the innovation ecosystem
Best Practices in Knowledge Transfer: Insights from Top Universities
The impact of knowledge transfer induced by universities on economy, society, and culture is widely acknowledged; nevertheless, this aspect is often neglected by university rankings. Here, we considered three of the most popular global university rankings and specific knowledge transfer indicators by U-multirank, a European ranking system launched by the European Commission, in order to answer to the following research question: how do the world top universities, evaluated according to global university rankings, perform from a knowledge transfer point of view? To this aim, the top universities have been compared with the others through the calculation of a Global Performance Indicator in Knowledge Transfer (GPI KT), a hierarchical clustering, and an outlier analysis. The results show that the universities best rated by global rankings do not always perform as well from knowledge transfer point of view. By combining the obtained results, it is possible to state that only 5 universities (Berkeley, Stanford, MIT, Harvard, CALTEC), among the top in the world, exhibit a high-level performance in knowledge transfer activities. For a better understanding of the success factors and best practices in knowledge transfer, a brief description of the 5 cited universities, in terms of organization of technology transfer service, relationship with business, entrepreneurship programs, and, more generally, third mission activities, is provided. A joint reading of the results suggests that the most popular global university rankings probably fail to effectively photograph third mission activities because they can manifest in a variety of forms, due to the intrinsic and intangible nature of third mission variables, which are difficult to quantify with simple and few indicators
Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research
Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; \u3c37 \u3eweeks) or (2) early preterm birth (ePTB; \u3c32 \u3eweeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth
Unexpected Diversity of Cellular Immune Responses against Nef and Vif in HIV-1-Infected Patients Who Spontaneously Control Viral Replication
Background: HIV-1-infected individuals who spontaneously control viral replication represent an example of successful containment of the AIDS virus. Understanding the anti-viral immune responses in these individuals may help in vaccine design. However, immune responses against HIV-1 are normally analyzed using HIV-1 consensus B 15-mers that overlap by 11 amino acids. Unfortunately, this method may underestimate the real breadth of the cellular immune responses against the autologous sequence of the infecting virus. Methodology and Principal Findings: Here we compared cellular immune responses against nef and vif-encoded consensus B 15-mer peptides to responses against HLA class I-predicted minimal optimal epitopes from consensus B and autologous sequences in six patients who have controlled HIV-1 replication. Interestingly, our analysis revealed that three of our patients had broader cellular immune responses against HLA class I-predicted minimal optimal epitopes from either autologous viruses or from the HIV-1 consensus B sequence, when compared to responses against the 15-mer HIV-1 type B consensus peptides. Conclusion and Significance: This suggests that the cellular immune responses against HIV-1 in controller patients may be broader than we had previously anticipated.National Institutes of Health (NIH)[R24 RR015371]Ministry of Health[914/BRA/3014-UNESCO]Sao Paulo City Health Department[2004-0.168.922-7]Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)[04/15856-9]Coordenacao de Aperfeicoamento de Pessoal de Ni-vel Superior (CAPES), Brazilian Ministry of Educatio
When do improved covariance matrix estimators enhance portfolio optimization? An empirical comparative study of nine estimators
The use of improved covariance matrix estimators as an alternative to the sample estimator is considered an important approach for enhancing portfolio optimization. Here we empirically compare the performance of nine improved covariance estimation procedures using daily returns of 90 highly capitalized US stocks for the period 1997-2007. We find that the usefulness of covariance matrix estimators strongly depends on the ratio between the estimation period T and the number of stocks N, on the presence or absence of short selling, and on the performance metric considered. When short selling is allowed, several estimation methods achieve a realized risk that is significantly smaller than that obtained with the sample covariance method. This is particularly true when T/N is close to one. Moreover, many estimators reduce the fraction of negative portfolio weights, while little improvement is achieved in the degree of diversification. On the contrary, when short selling is not allowed and T > N, the considered methods are unable to outperform the sample covariance in terms of realized risk, but can give much more diversified portfolios than that obtained with the sample covariance. When T < N, the use of the sample covariance matrix and of the pseudo-inverse gives portfolios with very poor performance.Portfolio optimization, Correlation structures, Statistical methods, Econophysics,
Etytree: A Graphical and Interactive Etymology Dictionary Based on Wiktionary
International audienceWe present etytree (from etymology + family tree): a new on-line multilingual tool to extract and visualize etymological relationships between words from the English Wiktionary. A first version of etytree is available at http://tools.wmflabs.org/etytree/. With etytree users can search a word and interactively explore etymologically related words (ancestors, descendants, cognates) in many languages using a graphical interface. The data is synchronised with the English Wiktionary dump at every new release, and can be queried via SPARQL from a Virtuoso endpoint. Etytree is the first graphical etymology dictionary, which could be used to search specific etymological definitions as well as to discover new relations among words. Moreover, it can be effectively adopted by Wiktionary editors to identify inconsistencies or missing information in the data. Etytree is a valuable tool for all those users interested in specific etymological definitions as well as for discovering new relations among words. Moreover, it can be effectively adopted by Wiktionary editors to identify inconsistencies or missing information in the data
When do improved covariance matrix estimators enhance portfolio optimization? An empirical comparative study of nine estimators
The use of improved covariance matrix estimators as an alternative to the sample estimator is considered an important approach for enhancing portfolio optimization. Here we empirically compare the performance of 9 improved covariance estimation procedures by using daily returns of 90 highly capitalized US stocks for the period 1997-2007. We find that the usefulness of covariance matrix estimators strongly depends on the ratio between estimation period T and number of stocks N, on the presence or absence of short selling, and on the performance metric considered. When short selling is allowed, several estimation methods achieve a realized risk that is significantly smaller than the one obtained with the sample covariance method. This is particularly true when T/N is close to one. Moreover many estimators reduce the fraction of negative portfolio weights, while little improvement is achieved in the degree of diversification. On the contrary when short selling is not allowed and T>N, the considered methods are unable to outperform the sample covariance in terms of realized risk but can give much more diversified portfolios than the one obtained with the sample covariance. When T