Rendezvous in Networks in Spite of Delay Faults

Two mobile agents, starting from different nodes of an unknown network, have to meet at the same node. Agents move in synchronous rounds using a deterministic algorithm. Each agent has a different label, which it can use in the execution of the algorithm, but it does not know the label of the other agent. Agents do not know any bound on the size of the network. In each round an agent decides if it remains idle or if it wants to move to one of the adjacent nodes. Agents are subject to delay faults: if an agent incurs a fault in a given round, it remains in the current node, regardless of its decision. If it planned to move and the fault happened, the agent is aware of it. We consider three scenarios of fault distribution: random (independently in each round and for each agent with constant probability 0 < p < 1), unbounded adver- sarial (the adversary can delay an agent for an arbitrary finite number of consecutive rounds) and bounded adversarial (the adversary can delay an agent for at most c consecutive rounds, where c is unknown to the agents). The quality measure of a rendezvous algorithm is its cost, which is the total number of edge traversals. For random faults, we show an algorithm with cost polynomial in the size n of the network and polylogarithmic in the larger label L, which achieves rendezvous with very high probability in arbitrary networks. By contrast, for unbounded adversarial faults we show that rendezvous is not feasible, even in the class of rings. Under this scenario we give a rendezvous algorithm with cost O(nl), where l is the smaller label, working in arbitrary trees, and we show that \Omega(l) is the lower bound on rendezvous cost, even for the two-node tree. For bounded adversarial faults, we give a rendezvous algorithm working for arbitrary networks, with cost polynomial in n, and logarithmic in the bound c and in the larger label L

Galactic Center gamma-ray "excess" from an active past of the Galactic Centre?

Several groups have recently claimed evidence for an unaccounted gamma-ray excess over {the} diffuse backgrounds at few GeV in {the} Fermi-LAT data in a region around the Galactic Center, consistent with a dark matter annihilation origin. We demonstrate that the main spectral and angular features of this excess can be reproduced if they are mostly due to inverse Compton emission from high-energy electrons injected in a burst event of ~10^52 - 10^53erg roughly O(10^6) years ago. We consider this example as a proof of principle that time-dependent phenomena need to be understood and accounted for - together with detailed diffuse foregrounds and unaccounted "steady state" astrophysical sources - before any robust inference can be made about dark matter signals at the Galactic Center. In addition, we point out that the timescale suggested by our study, which controls both the energy cutoff and the angular extension of the signal, intriguingly matches (together with the energy budget) what is indirectly inferred by other evidences suggesting a very active Galactic Center in the past, for instance related to intense star formation and accretion phenomena.Comment: 6 pages, 4 figures. Minor scale correction plus a typo in a figure label. Conclusions unchange

Active learning in annotating micro-blogs dealing with e-reputation

Elections unleash strong political views on Twitter, but what do people really think about politics? Opinion and trend mining on micro blogs dealing with politics has recently attracted researchers in several fields including Information Retrieval and Machine Learning (ML). Since the performance of ML and Natural Language Processing (NLP) approaches are limited by the amount and quality of data available, one promising alternative for some tasks is the automatic propagation of expert annotations. This paper intends to develop a so-called active learning process for automatically annotating French language tweets that deal with the image (i.e., representation, web reputation) of politicians. Our main focus is on the methodology followed to build an original annotated dataset expressing opinion from two French politicians over time. We therefore review state of the art NLP-based ML algorithms to automatically annotate tweets using a manual initiation step as bootstrap. This paper focuses on key issues about active learning while building a large annotated data set from noise. This will be introduced by human annotators, abundance of data and the label distribution across data and entities. In turn, we show that Twitter characteristics such as the author's name or hashtags can be considered as the bearing point to not only improve automatic systems for Opinion Mining (OM) and Topic Classification but also to reduce noise in human annotations. However, a later thorough analysis shows that reducing noise might induce the loss of crucial information.Comment: Journal of Interdisciplinary Methodologies and Issues in Science - Vol 3 - Contextualisation digitale - 201

Reducing Unlawful Prescription Drug Promotion: Is the Public Health Being Served by an Enforcement Approach that Focuses on Punishment?

Despite the imposition of increasingly substantial fines and recently successful efforts to impose individual liability on corporate executives under the Park doctrine, punishing pharmaceutical companies and their executives for unlawful promotional activities has not been as successful in achieving compliance with the Federal Food, Drug, and Cosmetic Act (FD&C Act) as the protection of the public health demands. Over the past decade, the Food and Drug Administration (FDA) and the Department of Justice (DOJ) have shifted their focus from correction and compliance to a more punitive model when it comes to allegedly unlawful promotion of pharmaceuticals. The shift initially focused on imposing monetary penalties and was arguably justified by the expectation that financial punishment would achieve a level of compliance that would reduce the need for correction. By exacting enormous fines from companies, the agencies presumably hoped that the costs associated with unlawful promotion would be too high to justify the monetary benefits of non-compliance. Unfortunately, however, that approach has not been entirely successful. Despite the growth in settlements and penalties, and the recent efforts to hold individual executives liable for corporate misbehavior, the intended impact of substantially increased compliance has only partially materialized. The upward spiraling of settlement amounts and the trend toward prosecuting repeat offenders indicate that a change in approach is necessary. This article argues that FDA and DOJ cannot justify a continued emphasis on punishment without more demonstrable improvement in compliance and corporate accountability. The article goes on to describe several proposals to refocus the agencies’ efforts to effectively address the impact of unlawful promotion on public health by returning to an approach that emphasizes the more traditional goals of correction and compliance. It also argues that any meaningful protection of the public health ultimately requires a broader public understanding of the issues surrounding unlawful promotion of pharmaceutical products and greater participation by patients; physicians; health care professionals; and others with an interest in, and the opportunity to, impact this area. Increasing the public’s ability and interest in monitoring companies’ promotional activities at every level will reinforce the benefits of compliance, which will better serve the public health goals of the FD&C Act

Active Sampling of Pairs and Points for Large-scale Linear Bipartite Ranking

Bipartite ranking is a fundamental ranking problem that learns to order relevant instances ahead of irrelevant ones. The pair-wise approach for bi-partite ranking construct a quadratic number of pairs to solve the problem, which is infeasible for large-scale data sets. The point-wise approach, albeit more efficient, often results in inferior performance. That is, it is difficult to conduct bipartite ranking accurately and efficiently at the same time. In this paper, we develop a novel active sampling scheme within the pair-wise approach to conduct bipartite ranking efficiently. The scheme is inspired from active learning and can reach a competitive ranking performance while focusing only on a small subset of the many pairs during training. Moreover, we propose a general Combined Ranking and Classification (CRC) framework to accurately conduct bipartite ranking. The framework unifies point-wise and pair-wise approaches and is simply based on the idea of treating each instance point as a pseudo-pair. Experiments on 14 real-word large-scale data sets demonstrate that the proposed algorithm of Active Sampling within CRC, when coupled with a linear Support Vector Machine, usually outperforms state-of-the-art point-wise and pair-wise ranking approaches in terms of both accuracy and efficiency.Comment: a shorter version was presented in ACML 201

Active learning and the Irish treebank

We report on our ongoing work in developing the Irish Dependency Treebank, describe the results of two Inter annotator Agreement (IAA) studies, demonstrate improvements in annotation consistency which have a knock-on effect on parsing accuracy, and present the final set of dependency labels. We then go on to investigate the extent to which active learning can play a role in treebank and parser development by comparing an active learning bootstrapping approach to a passive approach in which sentences are chosen at random for manual revision. We show that active learning outperforms passive learning, but when annotation effort is taken into account, it is not clear how much of an advantage the active learning approach has. Finally, we present results which suggest that adding automatic parses to the training data along with manually revised parses in an active learning setup does not greatly affect parsing accuracy