215 research outputs found
Client-server multi-task learning from distributed datasets
A client-server architecture to simultaneously solve multiple learning tasks
from distributed datasets is described. In such architecture, each client is
associated with an individual learning task and the associated dataset of
examples. The goal of the architecture is to perform information fusion from
multiple datasets while preserving privacy of individual data. The role of the
server is to collect data in real-time from the clients and codify the
information in a common database. The information coded in this database can be
used by all the clients to solve their individual learning task, so that each
client can exploit the informative content of all the datasets without actually
having access to private data of others. The proposed algorithmic framework,
based on regularization theory and kernel methods, uses a suitable class of
mixed effect kernels. The new method is illustrated through a simulated music
recommendation system
Citation gaming induced by bibliometric evaluation: a country-level comparative analysis
It is several years since national research evaluation systems around the
globe started making use of quantitative indicators to measure the performance
of researchers. Nevertheless, the effects on these systems on the behavior of
the evaluated researchers are still largely unknown. We attempt to shed light
on this topic by investigating how Italian researchers reacted to the
introduction in 2011 of national regulations in which key passages of
professional careers are governed by bibliometric indicators. A new inwardness
measure, able to gauge the degree of scientific self-referentiality of a
country, is defined as the proportion of citations coming from the country
itself compared to the total number of citations gathered by the country.
Compared to the trends of the other G10 countries in the period 2000-2016,
Italy's inwardness shows a net increase after the introduction of the new
evaluation rules. Indeed, globally and also for a large majority of the
research fields, Italy became the European country with the highest inwardness.
Possible explanations are proposed and discussed, concluding that the observed
trends are strongly suggestive of a generalized strategic use of citations,
both in the form of author self-citations and of citation clubs. We argue that
the Italian case offers crucial insights on the constitutive effects of
evaluation systems. As such, it could become a paradigmatic case in the debate
about the use of indicators in science-policy contexts
Do they agree? Bibliometric evaluation vs informed peer review in the Italian research assessment exercise
During the Italian research assessment exercise, the national agency ANVUR
performed an experiment to assess agreement between grades attributed to
journal articles by informed peer review (IR) and by bibliometrics. A sample of
articles was evaluated by using both methods and agreement was analyzed by
weighted Cohen's kappas. ANVUR presented results as indicating an overall
'good' or 'more than adequate' agreement. This paper re-examines the experiment
results according to the available statistical guidelines for interpreting
kappa values, by showing that the degree of agreement, always in the range
0.09-0.42 has to be interpreted, for all research fields, as unacceptable, poor
or, in a few cases, as, at most, fair. The only notable exception, confirmed
also by a statistical meta-analysis, was a moderate agreement for economics and
statistics (Area 13) and its sub-fields. We show that the experiment protocol
adopted in Area 13 was substantially modified with respect to all the other
research fields, to the point that results for economics and statistics have to
be considered as fatally flawed. The evidence of a poor agreement supports the
conclusion that IR and bibliometrics do not produce similar results, and that
the adoption of both methods in the Italian research assessment possibly
introduced systematic and unknown biases in its final results. The conclusion
reached by ANVUR must be reversed: the available evidence does not justify at
all the joint use of IR and bibliometrics within the same research assessment
exercise.Comment: in Scientometrics, 201
A letter on Ancaiani et al. âEvaluating scientific research in Italy: the 2004-10 research evaluation exerciseâ
This letter documents some problems in Ancaiani et al. (2015). Namely the evaluation of concordance, based on Cohen's kappa, reported by Ancaiani et al. was not computed on the whole random sample of 9,199 articles, but on a subset of 7,597 articles. The kappas relative to the whole random sample were in the range 0.07â0.15, indicating an unacceptable agreement between peer review and bibliometrics. The subset was obtained by non-random exclusion of all articles for which bibliometrics produced an uncertain classification; these raw data were not disclosed, so that concordance analysis is not reproducible. The VQR-weighted kappa for Area 13 reported by Ancaiani et al. is higher than that reported by Area 13 panel and confirmed by Bertocchi et al. (2015), a difference explained by the use, under the same name, of two different set of weights. Two values of kappa reported by Ancaiani et al. differ from the corresponding ones published in the official report. Results reported by Ancaiani et al. do not support a good concordance between peer review and bibliometrics. As a consequence, the use of both techniques introduced systematic distortions in the final results of the Italian research assessment exercise. The conclusion that it is possible to use both technique as interchangeable in a research assessment exercise appears to be unsound, by being based on a misinterpretation of the statistical significance of kappa values
Errors and secret data in the Italian research assessment exercise. A comment to a reply
Italy adopted a performance-based system for funding universities that is centered on the results of a national research assessment exercise, realized by a governmental agency (ANVUR). ANVUR evaluated papers by using âa dual system of evaluationâ, that is by informed peer review or by bibliometrics. In view of validating that system, ANVUR performed an experiment for estimating the agreement between informed review and bibliometrics. Ancaiani et al. (2015) presents the main results of the experiment. Alberto Baccini and De Nicolao (2017) documented in a letter, among other critical issues, that the statistical analysis was not realized on a random sample of articles. A reply to the letter has been published by Research Evaluation (Benedetto et al. 2017). This note highlights that in the reply there are (1) errors in data, (2) problems with ârepresentativenessâ of the sample, (3) unverifiable claims about weights used for calculating kappas, (4) undisclosed averaging procedures; (5) a statement about âsame protocol in all areasâ contradicted by official reports. Last but not least: the data used by the authors continue to be undisclosed. A general warning concludes: many recently published papers use data originating from Italian research assessment exercise. These data are not accessible to the scientific community and consequently these papers are not reproducible. They can be hardly considered as containing sound evidence at least until authors or ANVUR disclose the data necessary for replication
Peer review and bibliometric indicators just donât match upaccording to re-analysis of Italian research evaluation
The Italian research evaluation agency undertook an extensive analysis to compare the results of peer review and bibliometric indicators for research evaluation. Their findings suggested both indicators produced similar results. Researchers Alberto Baccini and Giuseppe De Nicolao re-examine these results and find notable disagreements between the two techniques of evaluation in the sample and outline below the major shortcoming in the Italian Agencyâs interpretation. Results from one technique will differ from those reached using the other
Correction of Italian under-reporting in the first COVID-19 wave via age-specific deconvolution of hospital admissions
When 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 computed 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.Comment: 19 pages, 4 main figures, 2 supplementary figures, 2 table
Regularized linear system identification using atomic, nuclear and kernel-based norms: the role of the stability constraint
Inspired by ideas taken from the machine learning literature, new
regularization techniques have been recently introduced in linear system
identification. In particular, all the adopted estimators solve a regularized
least squares problem, differing in the nature of the penalty term assigned to
the impulse response. Popular choices include atomic and nuclear norms (applied
to Hankel matrices) as well as norms induced by the so called stable spline
kernels. In this paper, a comparative study of estimators based on these
different types of regularizers is reported. Our findings reveal that stable
spline kernels outperform approaches based on atomic and nuclear norms since
they suitably embed information on impulse response stability and smoothness.
This point is illustrated using the Bayesian interpretation of regularization.
We also design a new class of regularizers defined by "integral" versions of
stable spline/TC kernels. Under quite realistic experimental conditions, the
new estimators outperform classical prediction error methods also when the
latter are equipped with an oracle for model order selection
vanilla-option-pricing: Pricing and market calibration for options on energy commodities
Abstract The Python package vanilla-option-pricing implements procedures to price European vanilla options under the Black framework, using different stochastic models for the underlying asset. Currently, the geometric Brownian motion, the OrnsteinâUhlenbeck process and a two-factor mean-reverting process are available. The library supports market calibration, providing tools to tune the parameters of the stochastic processes against a set of listed options. The intended audience for the package is made of researchers and practitioners interested in quantitative finance and energy derivatives
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