27,946 research outputs found
Photometric Constraints on the Redshift of z~10 candidate UDFj-39546284 from deeper WFC3/IR+ACS+IRAC observations over the HUDF
Ultra-deep WFC3/IR observations on the HUDF from the HUDF09 program revealed
just one plausible z~10 candidate UDFj-39546284. UDFj-39546284 had all the
properties expected of a galaxy at z~10 showing (1) no detection in the deep
ACS+WFC3 imaging data blueward of the F160W band, exhibiting (2) a blue
spectral slope redward of the break, and showing (3) no prominent detection in
deep IRAC observations. The new, similarly deep WFC3/IR HUDF12 F160W
observations over the HUDF09/XDF allow us to further assess this candidate.
These observations show that this candidate, previously only detected at ~5.9
sigma in a single band, clearly corresponds to a real source. It is detected at
~5.3 sigma in the new H-band data and at ~7.8 sigma in the full 85-orbit H-band
stack. Interestingly, the non-detection of the source (<1 sigma) in the new
F140W observations suggests a higher redshift. Formally, the best-fit redshift
of the source utilizing all the WFC3+ACS (and IRAC+K-band) observations is
11.8+/-0.3. However, we consider the z~12 interpretation somewhat unlikely,
since the source would either need to be ~20x more luminous than expected or
show very high-EW Ly-alpha emission (which seems improbable given the extensive
neutral gas prevalent early in the reionization epoch). Lower-redshift
solutions fail if only continuum models are allowed. Plausible lower-redshift
solutions require that the H-band flux be dominated by line emission such as
Halpha or [OIII] with extreme EWs. The tentative detection of line emission at
1.6 microns in UDFj-39546284 in a companion paper suggests that such emission
may have already been found.Comment: 6 pages, 4 figures, 1 table, accepted for publication in ApJ Letters,
updated to match the version in pres
Network Inspection for Detecting Strategic Attacks
This article studies a problem of strategic network inspection, in which a
defender (agency) is tasked with detecting the presence of multiple attacks in
the network. An inspection strategy entails monitoring the network components,
possibly in a randomized manner, using a given number of detectors. We
formulate the network inspection problem as a large-scale
bilevel optimization problem, in which the defender seeks to determine an
inspection strategy with minimum number of detectors that ensures a target
expected detection rate under worst-case attacks. We show that optimal
solutions of can be obtained from the equilibria of a
large-scale zero-sum game. Our equilibrium analysis involves both
game-theoretic and combinatorial arguments, and leads to a computationally
tractable approach to solve . Firstly, we construct an
approximate solution by utilizing solutions of minimum set cover (MSC) and
maximum set packing (MSP) problems, and evaluate its detection performance. In
fact, this construction generalizes some of the known results in network
security games. Secondly, we leverage properties of the optimal detection rate
to iteratively refine our MSC/MSP-based solution through a column generation
procedure. Computational results on benchmark water networks demonstrate the
scalability, performance, and operational feasibility of our approach. The
results indicate that utilities can achieve a high level of protection in
large-scale networks by strategically positioning a small number of detectors
Community Detection in Dynamic Networks via Adaptive Label Propagation
An adaptive label propagation algorithm (ALPA) is proposed to detect and
monitor communities in dynamic networks. Unlike the traditional methods by
re-computing the whole community decomposition after each modification of the
network, ALPA takes into account the information of historical communities and
updates its solution according to the network modifications via a local label
propagation process, which generally affects only a small portion of the
network. This makes it respond to network changes at low computational cost.
The effectiveness of ALPA has been tested on both synthetic and real-world
networks, which shows that it can successfully identify and track dynamic
communities. Moreover, ALPA could detect communities with high quality and
accuracy compared to other methods. Therefore, being low-complexity and
parameter-free, ALPA is a scalable and promising solution for some real-world
applications of community detection in dynamic networks.Comment: 16 pages, 11 figure
Put three and three together: Triangle-driven community detection
Community detection has arisen as one of the most relevant topics in the field of graph data mining due to its applications in many fields such as biology, social networks, or network traffic analysis. Although the existing metrics used to quantify the quality of a community work well in general, under some circumstances, they fail at correctly capturing such notion. The main reason is that these metrics consider the internal community edges as a set, but ignore how these actually connect the vertices of the community. We propose the Weighted Community Clustering (WCC), which is a new community metric that takes the triangle instead of the edge as the minimal structural motif indicating the presence of a strong relation in a graph. We theoretically analyse WCC in depth and formally prove, by means of a set of properties, that the maximization of WCC guarantees communities with cohesion and structure. In addition, we propose Scalable Community Detection (SCD), a community detection algorithm based on WCC, which is designed to be fast and scalable on SMP machines, showing experimentally that WCC correctly captures the concept of community in social networks using real datasets. Finally, using ground-truth data, we show that SCD provides better quality than the best disjoint community detection algorithms of the state of the art while performing faster.Peer ReviewedPostprint (author's final draft
Building Program Vector Representations for Deep Learning
Deep learning has made significant breakthroughs in various fields of
artificial intelligence. Advantages of deep learning include the ability to
capture highly complicated features, weak involvement of human engineering,
etc. However, it is still virtually impossible to use deep learning to analyze
programs since deep architectures cannot be trained effectively with pure back
propagation. In this pioneering paper, we propose the "coding criterion" to
build program vector representations, which are the premise of deep learning
for program analysis. Our representation learning approach directly makes deep
learning a reality in this new field. We evaluate the learned vector
representations both qualitatively and quantitatively. We conclude, based on
the experiments, the coding criterion is successful in building program
representations. To evaluate whether deep learning is beneficial for program
analysis, we feed the representations to deep neural networks, and achieve
higher accuracy in the program classification task than "shallow" methods, such
as logistic regression and the support vector machine. This result confirms the
feasibility of deep learning to analyze programs. It also gives primary
evidence of its success in this new field. We believe deep learning will become
an outstanding technique for program analysis in the near future.Comment: This paper was submitted to ICSE'1
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