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 $(\mathcal{P})$ 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 $(\mathcal{P})$ 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 $(\mathcal{P})$. 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