2,442 research outputs found
Epigenetics: What it is about?
Epigenetics has captured the attention of scientists in the past decades, yet
its scope has been continuously changing. In this paper, we give an overview on
how and why its definition has evolved and suggest several clarification on the
concepts used in this field, in particular, on the notions of epigenetic
information, epigenetic stability and epigenetic templating. Another issue that
we address is the role of epigenetic information. Not only it is important in
allowing alternative interpretations of genetic information, but it appears to
be important in protecting the genetic information, moreover, we suggest that
this function appeared first in evolution and only later on the epigenetic
mechanisms were recruited to play a role in cell differentiation.Comment: 7 pages, 3 figure
Spectral density of the non-backtracking operator
The non-backtracking operator was recently shown to provide a significant
improvement when used for spectral clustering of sparse networks. In this paper
we analyze its spectral density on large random sparse graphs using a mapping
to the correlation functions of a certain interacting quantum disordered system
on the graph. On sparse, tree-like graphs, this can be solved efficiently by
the cavity method and a belief propagation algorithm. We show that there exists
a paramagnetic phase, leading to zero spectral density, that is stable outside
a circle of radius , where is the leading eigenvalue of the
non-backtracking operator. We observe a second-order phase transition at the
edge of this circle, between a zero and a non-zero spectral density. That fact
that this phase transition is absent in the spectral density of other matrices
commonly used for spectral clustering provides a physical justification of the
performances of the non-backtracking operator in spectral clustering.Comment: 6 pages, 6 figures, submitted to EP
Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation
The completion of low rank matrices from few entries is a task with many
practical applications. We consider here two aspects of this problem:
detectability, i.e. the ability to estimate the rank reliably from the
fewest possible random entries, and performance in achieving small
reconstruction error. We propose a spectral algorithm for these two tasks
called MaCBetH (for Matrix Completion with the Bethe Hessian). The rank is
estimated as the number of negative eigenvalues of the Bethe Hessian matrix,
and the corresponding eigenvectors are used as initial condition for the
minimization of the discrepancy between the estimated matrix and the revealed
entries. We analyze the performance in a random matrix setting using results
from the statistical mechanics of the Hopfield neural network, and show in
particular that MaCBetH efficiently detects the rank of a large
matrix from entries, where is a constant close to .
We also evaluate the corresponding root-mean-square error empirically and show
that MaCBetH compares favorably to other existing approaches.Comment: NIPS Conference 201
Spectral Clustering of Graphs with the Bethe Hessian
Spectral clustering is a standard approach to label nodes on a graph by
studying the (largest or lowest) eigenvalues of a symmetric real matrix such as
e.g. the adjacency or the Laplacian. Recently, it has been argued that using
instead a more complicated, non-symmetric and higher dimensional operator,
related to the non-backtracking walk on the graph, leads to improved
performance in detecting clusters, and even to optimal performance for the
stochastic block model. Here, we propose to use instead a simpler object, a
symmetric real matrix known as the Bethe Hessian operator, or deformed
Laplacian. We show that this approach combines the performances of the
non-backtracking operator, thus detecting clusters all the way down to the
theoretical limit in the stochastic block model, with the computational,
theoretical and memory advantages of real symmetric matrices.Comment: 8 pages, 2 figure
Clustering from Sparse Pairwise Measurements
We consider the problem of grouping items into clusters based on few random
pairwise comparisons between the items. We introduce three closely related
algorithms for this task: a belief propagation algorithm approximating the
Bayes optimal solution, and two spectral algorithms based on the
non-backtracking and Bethe Hessian operators. For the case of two symmetric
clusters, we conjecture that these algorithms are asymptotically optimal in
that they detect the clusters as soon as it is information theoretically
possible to do so. We substantiate this claim for one of the spectral
approaches we introduce
Women & Whistleblowing
As more women in the United States take on leadership positions in the public and private sector, we have seen an influx of women whistleblowers. This Note examines whistleblower laws through a gender lens and offers insight to reveal why women blow the whistle, how women approach whistleblowing situations, and the effect current whistleblower laws have on women in the workforce.
This Note is divided into five parts. Part I includes an introduction and discusses competing definitions of whistleblower. Part II explores society’s differing perception of men and women whistleblowers and what may motivate women to report wrongdoings. Part III examines current whistle- blower laws. Part IV discusses aspects of the law that may affect women’s ability to blow the whistle, including external reporting, internal reporting, and retaliation provisions. Lastly, Part V discusses possible policy changes that can address the gaps in whistleblower laws and create a more effective avenue for women to blow the whistle.
This Note’s analysis is limited to the question of whether women and men approach the decision to blow the whistle differently and explores the way women respond to whistleblower laws. Therefore, the following anal- ysis is very limited and does not address the more comprehensive inquiry of characteristics beyond gender such as race, class, religion, national origin, or sexual orientation. Considering these ch racteristics is encour- aged and would provide a more complete analysis
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