688 research outputs found
Topology recognition with advice
In topology recognition, each node of an anonymous network has to
deterministically produce an isomorphic copy of the underlying graph, with all
ports correctly marked. This task is usually unfeasible without any a priori
information. Such information can be provided to nodes as advice. An oracle
knowing the network can give a (possibly different) string of bits to each
node, and all nodes must reconstruct the network using this advice, after a
given number of rounds of communication. During each round each node can
exchange arbitrary messages with all its neighbors and perform arbitrary local
computations. The time of completing topology recognition is the number of
rounds it takes, and the size of advice is the maximum length of a string given
to nodes.
We investigate tradeoffs between the time in which topology recognition is
accomplished and the minimum size of advice that has to be given to nodes. We
provide upper and lower bounds on the minimum size of advice that is sufficient
to perform topology recognition in a given time, in the class of all graphs of
size and diameter , for any constant . In most
cases, our bounds are asymptotically tight
Semi-supervised Convolutional Neural Networks for Identifying Wi-Fi Interference Sources
We present a convolutional neural network for identifying radio frequency devices from signal data, in order to detect possible interference sources for wireless local area networks. Collecting training data for this problem is particularly challenging due to a high number of possible interfering devices, difficulty in obtaining precise timings, and the need to measure the devices in varying conditions. To overcome this challenge we focus on semi-supervised learning, aiming to minimize the need for reliable training samples while utilizing larger amounts of unsupervised labels to improve the accuracy. In particular, we propose a novel structured extension of the pseudo-label technique to take advantage of temporal continuity in the data and show that already a few seconds of training data for each device is sufficient for highly accurate recognition.Peer reviewe
Global and Local Information in Clustering Labeled Block Models
The stochastic block model is a classical cluster-exhibiting random graph
model that has been widely studied in statistics, physics and computer science.
In its simplest form, the model is a random graph with two equal-sized
clusters, with intra-cluster edge probability p, and inter-cluster edge
probability q. We focus on the sparse case, i.e., p, q = O(1/n), which is
practically more relevant and also mathematically more challenging. A
conjecture of Decelle, Krzakala, Moore and Zdeborova, based on ideas from
statistical physics, predicted a specific threshold for clustering. The
negative direction of the conjecture was proved by Mossel, Neeman and Sly
(2012), and more recently the positive direction was proven independently by
Massoulie and Mossel, Neeman, and Sly.
In many real network clustering problems, nodes contain information as well.
We study the interplay between node and network information in clustering by
studying a labeled block model, where in addition to the edge information, the
true cluster labels of a small fraction of the nodes are revealed. In the case
of two clusters, we show that below the threshold, a small amount of node
information does not affect recovery. On the other hand, we show that for any
small amount of information efficient local clustering is achievable as long as
the number of clusters is sufficiently large (as a function of the amount of
revealed information).Comment: 24 pages, 2 figures. A short abstract describing these results will
appear in proceedings of RANDOM 201
Multi-Label Latent Spaces with Semi-Supervised Deep Generative Models
Expert labeling, tagging, and assessment are far more costly than the processes of collecting raw data. Generative modeling is a very powerful tool to tackle this real-world problem. It is shown here how these models can be used to allow for semi-supervised learning that performs very well in label-deficient conditions.
The foundation for the work in this dissertation is built upon visualizing generative models\u27 latent spaces to gain deeper understanding of data, analyze faults, and propose solutions. A number of novel ideas and approaches are presented to improve single-label classification. This dissertation\u27s main focus is on extending semi-supervised Deep Generative Models for solving the multi-label problem by proposing unique mathematical and programming concepts and organization.
In all naive mixtures, using multiple labels is detrimental and causes each label\u27s predictions to be worse than models that utilize only a single label. Examining latent spaces reveals that in many cases, large regions in the models generate meaningless results. Enforcing a priori independence is essential, and only when applied can multi-label models outperform the best single-label models. Finally, a novel learning technique called open-book learning is described that is capable of surpassing the state-of-the-art classification performance of generative models for multi-labeled, semi-supervised data sets
Mining team characteristics to predict Wikipedia article quality
International audienceIn this study, we were interested in studying which characteristics of virtual teams are good predictors for the quality of their production. The experiment involved obtaining the Spanish Wikipedia database dump and applying different data mining techniques suitable for large data sets to label the whole set of articles according to their quality (comparing them with the Featured/Good Articles, or FA/GA). Then we created the attributes that describe the characteristics of the team who produced the articles and using decision tree methods, we obtained the most relevant characteristics of the teams that produced FA/GA. The team's maximum efficiency and the total length of contribution are the most important predictors. This article contributes to the literature on virtual team organization
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