30,131 research outputs found
Direct, physically-motivated derivation of the contagion condition for spreading processes on generalized random networks
For a broad range single-seed contagion processes acting on generalized
random networks, we derive a unifying analytic expression for the possibility
of global spreading events in a straightforward, physically intuitive fashion.
Our reasoning lays bare a direct mechanical understanding of an archetypal
spreading phenomena that is not evident in circuitous extant mathematical
approaches.Comment: 4 pages, 1 figure, 1 tabl
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Archetypal landscapes for deep neural networks.
The predictive capabilities of deep neural networks (DNNs) continue to evolve to increasingly impressive levels. However, it is still unclear how training procedures for DNNs succeed in finding parameters that produce good results for such high-dimensional and nonconvex loss functions. In particular, we wish to understand why simple optimization schemes, such as stochastic gradient descent, do not end up trapped in local minima with high loss values that would not yield useful predictions. We explain the optimizability of DNNs by characterizing the local minima and transition states of the loss-function landscape (LFL) along with their connectivity. We show that the LFL of a DNN in the shallow network or data-abundant limit is funneled, and thus easy to optimize. Crucially, in the opposite low-data/deep limit, although the number of minima increases, the landscape is characterized by many minima with similar loss values separated by low barriers. This organization is different from the hierarchical landscapes of structural glass formers and explains why minimization procedures commonly employed by the machine-learning community can navigate the LFL successfully and reach low-lying solutions.A.A.L. was supported by the Winton Program for the Physics of Sustainability. P.C.V. and D.J.W. were supported by the Engineering and Physical Sciences Research Council
Unsupervised Learning of Artistic Styles with Archetypal Style Analysis
In this paper, we introduce an unsupervised learning approach to
automatically discover, summarize, and manipulate artistic styles from large
collections of paintings. Our method is based on archetypal analysis, which is
an unsupervised learning technique akin to sparse coding with a geometric
interpretation. When applied to deep image representations from a collection of
artworks, it learns a dictionary of archetypal styles, which can be easily
visualized. After training the model, the style of a new image, which is
characterized by local statistics of deep visual features, is approximated by a
sparse convex combination of archetypes. This enables us to interpret which
archetypal styles are present in the input image, and in which proportion.
Finally, our approach allows us to manipulate the coefficients of the latent
archetypal decomposition, and achieve various special effects such as style
enhancement, transfer, and interpolation between multiple archetypes.Comment: Accepted at NIPS 2018, Montr\'eal, Canad
Ridge Estimation of Inverse Covariance Matrices from High-Dimensional Data
We study ridge estimation of the precision matrix in the high-dimensional
setting where the number of variables is large relative to the sample size. We
first review two archetypal ridge estimators and note that their utilized
penalties do not coincide with common ridge penalties. Subsequently, starting
from a common ridge penalty, analytic expressions are derived for two
alternative ridge estimators of the precision matrix. The alternative
estimators are compared to the archetypes with regard to eigenvalue shrinkage
and risk. The alternatives are also compared to the graphical lasso within the
context of graphical modeling. The comparisons may give reason to prefer the
proposed alternative estimators
Analysis of heat kernel highlights the strongly modular and heat-preserving structure of proteins
In this paper, we study the structure and dynamical properties of protein
contact networks with respect to other biological networks, together with
simulated archetypal models acting as probes. We consider both classical
topological descriptors, such as the modularity and statistics of the shortest
paths, and different interpretations in terms of diffusion provided by the
discrete heat kernel, which is elaborated from the normalized graph Laplacians.
A principal component analysis shows high discrimination among the network
types, either by considering the topological and heat kernel based vector
characterizations. Furthermore, a canonical correlation analysis demonstrates
the strong agreement among those two characterizations, providing thus an
important justification in terms of interpretability for the heat kernel.
Finally, and most importantly, the focused analysis of the heat kernel provides
a way to yield insights on the fact that proteins have to satisfy specific
structural design constraints that the other considered networks do not need to
obey. Notably, the heat trace decay of an ensemble of varying-size proteins
denotes subdiffusion, a peculiar property of proteins
Users, Data, Networks A Proposal for Taxing the Digital Economy in the European Single Market. Bertelsmann Stiftung Policy Paper 12 March 2019
Fair taxation of digital businesses will be a key issue in the forthcoming European election campaign.
The debate will most likely revolve around the introduction of a new “digital tax” on companies’
turnover, as proposed by the European Commission, for example. In the short term this
may be a workable solution, but it does not solve the real underlying problem: the current rules
on corporate taxation in the EU are not fit for purpose when it comes to dealing with digital value
creation. That is what we want to change with our proposal.
To this end, we define clear criteria and principles for assessing digital value creation in company
taxation, which should apply EU-wide. Our contribution thus fills a central gap even of those current
proposals that do not envisage a new tax, but aim at a change in the system itself
Understanding the Heterogeneity of Contributors in Bug Bounty Programs
Background: While bug bounty programs are not new in software development, an
increasing number of companies, as well as open source projects, rely on
external parties to perform the security assessment of their software for
reward. However, there is relatively little empirical knowledge about the
characteristics of bug bounty program contributors. Aim: This paper aims to
understand those contributors by highlighting the heterogeneity among them.
Method: We analyzed the histories of 82 bug bounty programs and 2,504 distinct
bug bounty contributors, and conducted a quantitative and qualitative survey.
Results: We found that there are project-specific and non-specific contributors
who have different motivations for contributing to the products and
organizations. Conclusions: Our findings provide insights to make bug bounty
programs better and for further studies of new software development roles.Comment: 6 pages, ESEM 201
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