74 research outputs found
Face amplitude of spinfoam quantum gravity
The structure of the boundary Hilbert-space and the condition that amplitudes
behave appropriately under compositions determine the face amplitude of a
spinfoam theory. In quantum gravity the face amplitude turns out to be simpler
than originally thought.Comment: 5 pages, 2 figure
A Curie-Weiss model with dissipation
We consider stochastic dynamics for a spin system with mean field
interaction, in which the interaction potential is subject to noisy and
dissipative stochastic evolution. We show that, in the thermodynamic limit and
at sufficiently low temperature, the magnetization of the system has a time
periodic behavior, despite of the fact that no periodic force is applied
Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter
Microblogs are increasingly exploited for predicting prices and traded
volumes of stocks in financial markets. However, it has been demonstrated that
much of the content shared in microblogging platforms is created and publicized
by bots and spammers. Yet, the presence (or lack thereof) and the impact of
fake stock microblogs has never systematically been investigated before. Here,
we study 9M tweets related to stocks of the 5 main financial markets in the US.
By comparing tweets with financial data from Google Finance, we highlight
important characteristics of Twitter stock microblogs. More importantly, we
uncover a malicious practice - referred to as cashtag piggybacking -
perpetrated by coordinated groups of bots and likely aimed at promoting
low-value stocks by exploiting the popularity of high-value ones. Among the
findings of our study is that as much as 71% of the authors of suspicious
financial tweets are classified as bots by a state-of-the-art spambot detection
algorithm. Furthermore, 37% of them were suspended by Twitter a few months
after our investigation. Our results call for the adoption of spam and bot
detection techniques in all studies and applications that exploit
user-generated content for predicting the stock market
Fair Enough? A map of the current limitations of the requirements to have "fair" algorithms
In the recent years, the raise in the usage and efficiency of Artificial
Intelligence and, more in general, of Automated Decision-Making systems has
brought with it an increasing and welcome awareness of the risks associated
with such systems. One of such risks is that of perpetuating or even amplifying
bias and unjust disparities present in the data from which many of these
systems learn to adjust and optimise their decisions. This awareness has on one
side encouraged several scientific communities to come up with more and more
appropriate ways and methods to assess, quantify, and possibly mitigate such
biases and disparities. On the other hand, it has prompted more and more layers
of society, including policy makers, to call for "fair" algorithms. We believe
that while a lot of excellent and multidisciplinary research is currently being
conducted, what is still fundamentally missing is the awareness that having
"fair" algorithms is per se a nearly meaningless requirement, that needs to be
complemented with a lot of additional societal choices to become actionable.
Namely, there is a hiatus between what the society is demanding from Automated
Decision-Making systems, and what this demand actually means in real-world
scenarios. In this work, we outline the key features of such a hiatus, and
pinpoint a list of fundamental ambiguities and attention points that we as a
society must address in order to give a concrete meaning to the increasing
demand of fairness in Automated Decision-Making systems.Comment: 20 pages, 2 figures, 2 tables. V2: added reference, update info on AI
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Oxidative responsiveness to multiple stressors in the key Antarctic species, Adamussium colbecki: interactions between temperature, acidification and cadmium exposure.
Abstract: High-latitude marine ecosystems are ranked to be among the most
sensitive regions to climate change since highly stenothermal and
specially adapted organisms might be seriously affected by global warming
and ocean acidification. The present investigation was aimed to provide
new insights on the sensitivity to such environmental stressors in the
key Antarctic species, Adamussium colbecki, focussing also on their
synergistic effects with cadmium exposure, naturally abundant in this
area for upwelling phenomena. Scallops were exposed for 2 weeks to
various combinations of Cd (0 and 40 μgL-1), pH (8.05 and 7.60) and
temperature (-1 and +1°C). Beside Cd bioaccumulation, a wide panel of
early warning biomarkers were analysed in digestive glands and gills
including levels of metallothioneins, individual antioxidants and total
oxyradical scavenging capacity, onset of oxidative cell damage like lipid
peroxidation, lysosomal stability, DNA integrity and peroxisomal
proliferation. Results indicated reciprocal interactions between multiple
stressors and their elaboration by a quantitative hazard model based on
the relevance and magnitude of effects, highlighted a different
sensitivity of analysed tissues. Due to cellular adaptations to high
basal Cd content, digestive gland appeared more tolerant toward other
prooxidant stressors, but sensitive to variations of the metal. On the
other hand, gills were more affected by various combinations of stressors
occurring at higher temperatur
Bias on demand : a modelling framework that generates synthetic data with bias
Nowadays, Machine Learning (ML) systems are widely used in various businesses and are increasingly being adopted to make decisions that can significantly impact people’s lives. However, these decision-making systems rely on data-driven learning, which poses a risk of propagating the bias embedded in the data. Despite various attempts by the algorithmic fairness community to outline different types of bias in data and algorithms, there is still a limited understanding of how these biases relate to the fairness of ML-based decision-making systems. In addition, efforts to mitigate bias and unfairness are often agnostic to the specific type(s) of bias present in the data. This paper explores the nature of fundamental types of bias, discussing their relationship to moral and technical frameworks. To prevent harmful consequences, it is essential to comprehend how and where bias is introduced throughout the entire modelling pipeline and possibly how to mitigate it. Our primary contribution is a framework for generating synthetic datasets with different forms of biases. We use our proposed synthetic data generator to perform experiments on different scenarios to showcase the interconnection between biases and their effect on performance and fairness evaluations. Furthermore, we provide initial insights into mitigating specific types of bias through post-processing techniques. The implementation of the synthetic data generator and experiments can be found at https://github.com/rcrupiISP/BiasOnDemand
Phantom without phantom or how the PT symmetry saves us from the Big Rip
We consider the PT symmetric flat Friedmann model of two scalar fields with
positive kinetic terms. While the potential of one ("normal") field is taken
real, that of the other field is complex. We study a complex classical solution
of the system of the two Klein-Gordon equations together with the Friedmann
equation. The solution for the normal field is real while the solution for the
second field is purely imaginary, realizing classically the "phantom" behavior.
The energy density and pressure are real and the corresponding geometry is
well-defined. The Lagrangian for the linear perturbations has the correct
potential signs for both the fields, so that the problem of stability does not
arise. The background dynamics is determined by an effective action including
two real fields one normal and one "phantom". Remarkably, the phantom phase in
the cosmological evolution is transient and the Big Rip never occurs. Our model
is contrasted to well-known quintom models, which also include one normal and
one phantom fields.Comment: revised and enlarged version, to be published in Int. J. Mod. Phys.
D, the title is change
Application of a Weight of Evidence Approach for Monitoring Complex Environmental Scenarios: the Case-Study of Off-Shore Platforms
Multidisciplinary investigations based on integration of chemical and biological measurements, represent an added value to monitoring and management protocols, and their use is recommended by European Directives to evaluate the environmental status of aquatic ecosystems. However, assessing the overall significance of results obtained in different typologies of studies is often a difficult challenge. The aim of this work was to present a quantitative Weight Of Evidence (WOE) model (Sediqualsoft) to integrate huge amounts of heterogeneous data and to validate this approach in complex monitoring scenarios. Using the case-study of an off-shore platform field in the Adriatic Sea, procedures are presented to elaborate different typologies of data (lines of evidence, LOEs), including chemical characterization of sediments, bioavailability, biomarkers, ecotoxicological bioassays and benthic communities around three platforms. These data are initially evaluated by logical flowcharts and mathematical algorithms, which provide specific hazard indices for each considered LOE, before their different weighting and overall integration in an environmental risk index. The monitoring study selected for the WOE elaboration consisted on chemical analyses of trace metals, aliphatic hydrocarbons, polycyclic aromatic hydrocarbons carried out on 60 sediment samples; the same samples were also characterized for the status of benthic communities; bioavailability of metals from sediments was assessed in laboratory conditions on the polychaete Hediste diversicolor, while bioaccumulation of inorganic and organic chemicals and biomarker responses were measured in native and transplanted mussels; ecotoxicological properties of sediments were evaluated through a battery of bioassays determining algal growth of the diatom Phaeodactylum tricornutum, bioluminescence of the marine bacterium Vibrio fischeri, survival of the copepod Acartia tonsa and embryotoxicity of sea urchin Paracentrotus lividus. Overall, almost 7000 analytical results were elaborated and summarized in specific hazard indices. The WOE integration of multiple typologies of data allowed more robust and weighted conclusions compared to the use of individual LOEs, highlighting the feasibility of this procedure for multidisciplinary monitoring and risk assessment approaches. On a practical side, the WOE evidences also suggested a revision of actual monitoring procedures. Overall, the proposed WOE model appeared as a useful tool to summarize large datasets of complex data in integrative indices, and to simplify the interpretation for stakeholders and decision makers, thus supporting a more comprehensive process of "site-oriented" management decisions
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