181 research outputs found
The role of idiotypic interactions in the adaptive immune system: a belief-propagation approach
In this work we use belief-propagation techniques to study the equilibrium
behaviour of a minimal model for the immune system comprising interacting T and
B clones. We investigate the effect of the so-called idiotypic interactions
among complementary B clones on the system's activation. Our result shows that
B-B interactions increase the system's resilience to noise, making clonal
activation more stable, while increasing the cross-talk between different
clones. We derive analytically the noise level at which a B clone gets
activated, in the absence of cross-talk, and find that this increases with the
strength of idiotypic interactions and with the number of T cells signalling
the B clone. We also derive, analytically and numerically, via population
dynamics, the critical line where clonal cross-talk arises. Our approach allows
us to derive the B clone size distribution, which can be experimentally
measured and gives important information about the adaptive immune system
response to antigens and vaccination.Comment: 37 pages, 18 figure
SHARVOT: secret SHARe-based VOTing on the blockchain
Recently, there has been a growing interest in using online technologies to
design protocols for secure electronic voting. The main challenges include vote
privacy and anonymity, ballot irrevocability and transparency throughout the
vote counting process. The introduction of the blockchain as a basis for
cryptocurrency protocols, provides for the exploitation of the immutability and
transparency properties of these distributed ledgers.
In this paper, we discuss possible uses of the blockchain technology to
implement a secure and fair voting system. In particular, we introduce a secret
share-based voting system on the blockchain, the so-called SHARVOT protocol.
Our solution uses Shamir's Secret Sharing to enable on-chain, i.e. within the
transactions script, votes submission and winning candidate determination. The
protocol is also using a shuffling technique, Circle Shuffle, to de-link voters
from their submissions.Comment: WETSEB'18:IEEE/ACM 1st International Workshop on Emerging Trends in
Software Engineering for Blockchain. 5 pages, 2 figure
Item selection by Latent Class-based methods
The evaluation of nursing homes is usually based on the administration of
questionnaires made of a large number of polytomous items. In such a context,
the Latent Class (LC) model represents a useful tool for clustering subjects in
homogenous groups corresponding to different degrees of impairment of the
health conditions. It is known that the performance of model-based clustering
and the accuracy of the choice of the number of latent classes may be affected
by the presence of irrelevant or noise variables. In this paper, we show the
application of an item selection algorithm to real data collected within a
project, named ULISSE, on the quality-of-life of elderly patients hosted in
italian nursing homes. This algorithm, which is closely related to that
proposed by Dean and Raftery in 2010, is aimed at finding the subset of items
which provides the best clustering according to the Bayesian Information
Criterion. At the same time, it allows us to select the optimal number of
latent classes. Given the complexity of the ULISSE study, we perform a
validation of the results by means of a sensitivity analysis to different
specifications of the initial subset of items and of a resampling procedure
A bivariate finite mixture growth model with selection
AbstractA model is proposed to analyze longitudinal data where two response variables are available, one of which is a binary indicator of selection and the other is continuous and observed only if the first is equal to 1. The model also accounts for individual covariates and may be considered as a bivariate finite mixture growth model as it is based on three submodels: (i) a probit model for the selection variable; (ii) a linear model for the continuous variable; and (iii) a multinomial logit model for the class membership. To suitably address endogeneity, the first two components rely on correlated errors as in a standard selection model. The proposed approach is applied to the analysis of the dynamics of household portfolio choices based on an unbalanced panel dataset of Italian households over the 1998β2014 period. For this dataset, we identify three latent classes of households with specific investment behaviors and we assess the effect of individual characteristics on households' portfolio choices. Our empirical findings also confirm the need to jointly model risky asset market participation and the conditional portfolio share to properly analyze investment behaviors over the life-cycle
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