Automatic Classification of Text Databases through Query Probing

Many text databases on the web are "hidden" behind search interfaces, and their documents are only accessible through querying. Search engines typically ignore the contents of such search-only databases. Recently, Yahoo-like directories have started to manually organize these databases into categories that users can browse to find these valuable resources. We propose a novel strategy to automate the classification of search-only text databases. Our technique starts by training a rule-based document classifier, and then uses the classifier's rules to generate probing queries. The queries are sent to the text databases, which are then classified based on the number of matches that they produce for each query. We report some initial exploratory experiments that show that our approach is promising to automatically characterize the contents of text databases accessible on the web.Comment: 7 pages, 1 figur

Effects of space shuttle launches STS-1 through STS-9 on terrestrial vegetation of John F. Kennedy Space Center, Florida

Space Shuttle launches produce a cloud containing hydrochloric acid (HCl), aluminum oxide (Al203), and other substances. Acidities of less than 0.5 pH have been measured routinely in association with the launch cloud. In an area of about 22 ha regularly exposed to the exhaust cloud during most Shuttle launches, acute vegetation damage has resulted from the first nine Shuttle launches. Changes include loss of sensitive species, loss of plant community structure, reduction in total cover, and replacement of some species by weedy invaders. Community level changes define a retrogressive sequence. One-time impacts to strand and dune vegetation occurred after launches of STS-8 and STS-9. Acute vegetation damage occurred especially to sensitive species. Within six months, however, recovery was nearly complete. Sensitivity of species to the launch cloud was partially predicted by previous laboratory studies. Far-field acidic and dry fallout from the cloud as it rises to stabilization and moves with the prevailing winds causes vegetation spotting. Damage from this deposition is minor; typically at most 1% to 5% of leaf surface area is affected. No plant mortality or community changes have occurred from far-field deposition

Entropy/IP: Uncovering Structure in IPv6 Addresses

In this paper, we introduce Entropy/IP: a system that discovers Internet address structure based on analyses of a subset of IPv6 addresses known to be active, i.e., training data, gleaned by readily available passive and active means. The system is completely automated and employs a combination of information-theoretic and machine learning techniques to probabilistically model IPv6 addresses. We present results showing that our system is effective in exposing structural characteristics of portions of the IPv6 Internet address space populated by active client, service, and router addresses. In addition to visualizing the address structure for exploration, the system uses its models to generate candidate target addresses for scanning. For each of 15 evaluated datasets, we train on 1K addresses and generate 1M candidates for scanning. We achieve some success in 14 datasets, finding up to 40% of the generated addresses to be active. In 11 of these datasets, we find active network identifiers (e.g., /64 prefixes or `subnets') not seen in training. Thus, we provide the first evidence that it is practical to discover subnets and hosts by scanning probabilistically selected areas of the IPv6 address space not known to contain active hosts a priori.Comment: Paper presented at the ACM IMC 2016 in Santa Monica, USA (https://dl.acm.org/citation.cfm?id=2987445). Live Demo site available at http://www.entropy-ip.com

Rapid quantitative pharmacodynamic imaging by a novel method: theory, simulation testing and proof of principle

Pharmacological challenge imaging has mapped, but rarely quantified, the sensitivity of a biological system to a given drug. We describe a novel method called rapid quantitative pharmacodynamic imaging. This method combines pharmacokinetic-pharmacodynamic modeling, repeated small doses of a challenge drug over a short time scale, and functional imaging to rapidly provide quantitative estimates of drug sensitivity including EC50 (the concentration of drug that produces half the maximum possible effect). We first test the method with simulated data, assuming a typical sigmoidal dose-response curve and assuming imperfect imaging that includes artifactual baseline signal drift and random error. With these few assumptions, rapid quantitative pharmacodynamic imaging reliably estimates EC50 from the simulated data, except when noise overwhelms the drug effect or when the effect occurs only at high doses. In preliminary fMRI studies of primate brain using a dopamine agonist, the observed noise level is modest compared with observed drug effects, and a quantitative EC50 can be obtained from some regional time-signal curves. Taken together, these results suggest that research and clinical applications for rapid quantitative pharmacodynamic imaging are realistic.Comment: 26 pages total, 4 tables, 10 figures. The original PDF file at https://peerj.com/articles/117/ includes active hyperlinks. This version is the final published version. (Differs from v2 only in that I corrected the abstract on the arXiv.org page.

Aspects of the FM Kondo Model: From Unbiased MC Simulations to Back-of-an-Envelope Explanations

Effective models are derived from the ferromagnetic Kondo lattice model with classical corespins, which greatly reduce the numerical effort. Results for these models are presented. They indicate that double exchange gives the correct order of magnitude and the correct doping dependence of the Curie temperature. Furthermore, we find that the jump in the particle density previously interpreted as phase separation is rather explained by ferromagnetic polarons.Comment: Proceedings of Wandlitz Days of Magnetism 200

Bayesian Network Structure Learning with Permutation Tests

In literature there are several studies on the performance of Bayesian network structure learning algorithms. The focus of these studies is almost always the heuristics the learning algorithms are based on, i.e. the maximisation algorithms (in score-based algorithms) or the techniques for learning the dependencies of each variable (in constraint-based algorithms). In this paper we investigate how the use of permutation tests instead of parametric ones affects the performance of Bayesian network structure learning from discrete data. Shrinkage tests are also covered to provide a broad overview of the techniques developed in current literature.Comment: 13 pages, 4 figures. Presented at the Conference 'Statistics for Complex Problems', Padova, June 15, 201

The Anomalous Infrared Emission of Abell 58

We present a new model to explain the excess in mid and near infrared emission of the central, hydrogen poor dust knot in the planetary nebula (PN) Abell 58. Current models disagree with ISO measurement because they apply an average grain size and equilibrium conditions only. We investigate grain size distributions and temperature fluctuations affecting infrared emission using a new radiative transfer code and discuss in detail the conditions requiring an extension of the classical description. The peculiar infrared emission of V605 Aql, the central dust knot in Abell 58, has been modeled with our code. V605 Aql is of special interest as it is one of only three stars ever observed to move from the evolutionary track of a central PN star back to the post-AGB state.Comment: 17 pages, 4 figures; accepted and to be published in Ap