6 research outputs found
Subjectivity and complexity of facial attractiveness
The origin and meaning of facial beauty represent a longstanding puzzle.
Despite the profuse literature devoted to facial attractiveness, its very
nature, its determinants and the nature of inter-person differences remain
controversial issues. Here we tackle such questions proposing a novel
experimental approach in which human subjects, instead of rating natural faces,
are allowed to efficiently explore the face-space and 'sculpt' their favorite
variation of a reference facial image. The results reveal that different
subjects prefer distinguishable regions of the face-space, highlighting the
essential subjectivity of the phenomenon.The different sculpted facial vectors
exhibit strong correlations among pairs of facial distances, characterising the
underlying universality and complexity of the cognitive processes, and the
relative relevance and robustness of the different facial distances.Comment: 15 pages, 5 figures. Supplementary information: 26 pages, 13 figure
Adapting to Disruptions: Flexibility as a Pillar of Supply Chain Resilience
Supply chain disruptions cause shortages of raw material and products. To
increase resilience, i.e., the ability to cope with shocks, substituting goods
in established supply chains can become an effective alternative to creating
new distribution links. We demonstrate its impact on supply deficits through a
detailed analysis of the US opioid distribution system. Reconstructing 40
billion empirical distribution paths, our data-driven model allows a unique
inspection of policies that increase the substitution flexibility. Our approach
enables policymakers to quantify the trade-off between increasing flexibility,
i.e., reduced supply deficits, and increasing complexity of the supply chain,
which could make it more expensive to operate
Unsupervised inference approach to facial attractiveness
The perception of facial beauty is a complex phenomenon depending on many,
detailed and global facial features influencing each other. In the machine
learning community this problem is typically tackled as a problem of supervised
inference. However, it has been conjectured that this approach does not capture
the complexity of the phenomenon. A recent original experiment
(Ib\'a\~nez-Berganza et al., Scientific Reports 9, 8364, 2019) allowed
different human subjects to navigate the face-space and ``sculpt'' their
preferred modification of a reference facial portrait. Here we present an
unsupervised inference study of the set of sculpted facial vectors in that
experiment. We first infer minimal, interpretable, and faithful probabilistic
models (through Maximum Entropy and artificial neural networks) of the
preferred facial variations, that capture the origin of the observed
inter-subject diversity in the sculpted faces. The application of such
generative models to the supervised classification of the gender of the
sculpting subjects, reveals an astonishingly high prediction accuracy. This
result suggests that much relevant information regarding the subjects may
influence (and be elicited from) her/his facial preference criteria, in
agreement with the multiple motive theory of attractiveness proposed in
previous works.Comment: main article (10 pages, 4 figures) + supplementary information (22
pages, 10 figures). minor typos corrected. Federico Maggiore added as autho
Modeling the Dynamics of Distribution Networks: A Data-Driven Approach to Supply Chains
This thesis examines the formation, growth, and resilience of large-scale distribution systems. We investigate the interactions among manufacturers, distributors, and consumers, and show how these interactions shape the growth and resilience of these systems. Our study begins with an empirical analysis, where we reconstruct the complete distribution networks of opioids in the United States using data from nearly half a billion shipping records. We then examine the main topological properties of these networks and analyze their stability over a nine-year period. Surprisingly, we find that despite the increasing demand for opioids, the main topological properties of the distribution networks remain stable. To investigate how distribution systems form and evolve, we develop an evolutionary network growth model that simulates strategic link formation between firms. Testing the model against the empirical data, we show that two mechanisms are essential for the emergence of the observed networks: centralization and multi-sourcing.
While centralization enhances efficiency, multi-sourcing fosters local resilience to shocks. Next, we discuss firm growth dynamics and examine how previous economic theories can be applied to the supply chain domain. Finally, we analyze system resilience to possible disruptions. We model the propagation of supply shocks at the firm-level and discuss various system responses to mitigate them. Our focus is on the role of supply substitution as a quick strategy that we show can effectively reduce the shock impact. Our research offers a valuable tool for managers and policymakers, enabling them to devise effective mitigation strategies that can be implemented after disruptions occur. Through a rigorous approach that combines both empirical analysis and data-driven modeling, we are able to unveil the underlying mechanisms that govern these systems. Our results contribute to both network science and supply chain management. In our attempt to bridge the gap between the two fields, we provide new methodologies based on high-resolution data to study the dynamics of large-scale distribution networks
Unsupervised inference approach to facial attractiveness
The perception of facial attractiveness is a complex phenomenon which depends on how the observer perceives not only individual facial features, but also their mutual influence and interplay. In the machine learning community, this problem is typically tackled as a problem of regression of the subject-averaged rating assigned to natural faces. However, it has been conjectured that this approach does not capture the complexity of the phenomenon. It has recently been shown that different human subjects can navigate the face-space and ââsculptââ their preferred modification of a reference facial portrait. Here we present an unsupervised inference study of the set of sculpted facial vectors in such experiments. We first infer minimal, interpretable and accurate probabilistic models (through Maximum Entropy and artificial neural networks) of the preferred facial variations, that encode the inter-subject variance. The application of such generative models to the supervised classification of the gender of the subject that sculpted the face reveals that it may be predicted with astonishingly high accuracy. We observe that the classification accuracy improves by increasing the order of the non-linear effective interaction. This suggests that the cognitive mechanisms related to facial discrimination in the brain do not involve the positions of single facial landmarks only, but mainly the mutual influence of couples, and even triplets and quadruplets of landmarks. Furthermore, the high prediction accuracy of the subjectsâ gender suggests that much relevant information regarding the subjects may influence (and be elicited from) their facial preference criteria, in agreement with the multiple motive theory of attractiveness proposed in previous works
Unsupervised inference approach to facial attractiveness
The perception of facial attractiveness is a complex phenomenon which depends on how the observer perceives not only individual facial features, but also their mutual influence and interplay. In the machine learning community, this problem is typically tackled as a problem of regression of the subject-averaged rating assigned to natural faces. However, it has been conjectured that this approach does not capture the complexity of the phenomenon. It has recently been shown that different human subjects can navigate the face-space and âsculptâ their preferred modification of a reference facial portrait. Here we present an unsupervised inference study of the set of sculpted facial vectors in such experiments. We first infer minimal, interpretable and accurate probabilistic models (through Maximum Entropy and artificial neural networks) of the preferred facial variations, that encode the inter-subject variance. The application of such generative models to the supervised classification of the gender of the subject that sculpted the face reveals that it may be predicted with astonishingly high accuracy. We observe that the classification accuracy improves by increasing the order of the non-linear effective interaction. This suggests that the cognitive mechanisms related to facial discrimination in the brain do not involve the positions of single facial landmarks only, but mainly the mutual influence of couples, and even triplets and quadruplets of landmarks. Furthermore, the high prediction accuracy of the subjectsâ gender suggests that much relevant information regarding the subjects may influence (and be elicited from) their facial preference criteria, in agreement with the multiple motive theory of attractiveness proposed in previous works.ISSN:2167-835