Covering problems in edge- and node-weighted graphs

This paper discusses the graph covering problem in which a set of edges in an edge- and node-weighted graph is chosen to satisfy some covering constraints while minimizing the sum of the weights. In this problem, because of the large integrality gap of a natural linear programming (LP) relaxation, LP rounding algorithms based on the relaxation yield poor performance. Here we propose a stronger LP relaxation for the graph covering problem. The proposed relaxation is applied to designing primal-dual algorithms for two fundamental graph covering problems: the prize-collecting edge dominating set problem and the multicut problem in trees. Our algorithms are an exact polynomial-time algorithm for the former problem, and a 2-approximation algorithm for the latter problem, respectively. These results match the currently known best results for purely edge-weighted graphs.Comment: To appear in SWAT 201

Radio Observations of the January 20, 2005 X-Class Event

We present a multi-frequency and multi-instrument study of the 20 January 2005 event. We focus mainly on the complex radio signatures and their association with the active phenomena taking place: flares, CMEs, particle acceleration and magnetic restructuring. As a variety of energetic particle accelerators and sources of radio bursts are present, in the flare-ejecta combination, we investigate their relative importance in the progress of this event. The dynamic spectra of {Artemis-IV-Wind/Waves-Hiras with 2000 MHz-20 kHz frequency coverage, were used to track the evolution of the event from the low corona to the interplanetary space; these were supplemented with SXR, HXR and gamma-ray recordings. The observations were compared with the expected radio signatures and energetic-particle populations envisaged by the {Standard Flare--CME model and the reconnection outflow termination shock model. A proper combination of these mechanisms seems to provide an adequate model for the interpretation of the observational data.Comment: Accepted for publication in Solar Physic

Investigation of the ferromagnetic transition in the correlated 4d perovskites SrRu1−x_{1-x}Rhx_xO3_3

The solid-solution SrRu$_{1-x}$Rh$_x$O$_3$ ($0\le x \le1$) is a variable-electron-configuration system forming in the nearly-cubic-perovskite basis, ranging from the ferromagnetic 4$d^4$ to the enhanced paramagnetic 4$d^5$. Polycrystalline single-phase samples were obtained over the whole composition range by a high-pressure-heating technique, followed by measurements of magnetic susceptibility, magnetization, specific heat, thermopower, and electrical resistivity. The ferromagnetic order in long range is gradually suppressed by the Rh substitution and vanishes at $x \sim 0.6$. The electronic term of specific-heat shows unusual behavior near the critical Rh concentration; the feature does not match even qualitatively with what was reported for the related perovskites (Sr,Ca)RuO$_3$. Furthermore, another anomaly in the specific heat was observed at $x \sim 0.9$.Comment: Accepted for publication in PR

Solving Problems of Practice in Education

The authors identify and discuss the many complexities involved in the translation of scientific information in the social sciences into forms usable for solving problems of practice in education. As a means of appropriately handling these complexities and the issues that arise, they prescribe a series of stages to be followed from the advent of a practitioner's situational problem to the design of a response to it. They assert that unless the process of translation is conducted with the prescribed level of understanding, appreciation, and rigor, the application of knowledge will be inaccurate.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68934/2/10.1177_107554708400600103.pd

Characterization of neutrino signals with radiopulses in dense media through the LPM effect

We discuss the possibilities of detecting radio pulses from high energy showers in ice, such as those produced by PeV and EeV neutrino interactions. It is shown that the rich radiation pattern structure in the 100 MHz to few GHz allows the separation of electromagnetic showers induced by photons or electrons above 100 PeV from those induced by hadrons. This opens up the possibility of measuring the energy fraction transmitted to the electron in a charged current electron neutrino interaction with adequate sampling of the angular distribution of the signal. The radio technique has the potential to complement conventional high energy neutrino detectors with flavor information.Comment: 5 pages, 4 ps figures. Submitted to Phys. Rev. Let

Signatures of star-planet interactions across the electromagnetic spectrum

Stars and planetary system

STATIC FOUR-DIMENSIONAL ABELIAN BLACK HOLES IN KALUZA-KLEIN THEORY

Static, four-dimensional (4-d) black holes (BH's) in ($4+n$)-d Kaluza-Klein (KK) theory with Abelian isometry and diagonal internal metric have at most one electric ($Q$) and one magnetic ($P$) charges, which can either come from the same $U(1)$-gauge field (corresponding to BH's in effective 5-d KK theory) or from different ones (corresponding to BH's with $U(1)_M\times U(1)_E$ isometry of an effective 6-d KK theory). In the latter case, explicit non-extreme solutions have the global space-time of Schwarzschild BH's, finite temperature, and non-zero entropy. In the extreme (supersymmetric) limit the singularity becomes null, the temperature saturates the upper bound $T_H=1/4\pi\sqrt{|QP|}$, and entropy is zero. A class of KK BH's with constrained charge configurations, exhibiting a continuous electric-magnetic duality, are generated by global $SO(n)$ transformations on the above classes of the solutions.Comment: 11 pages, 2 Postscript figures. uses RevTeX and psfig.sty (for figs) paper and figs also at ftp://dept.physics.upenn.edu/pub/Cvetic/UPR-645-

Plusmine: Dynamic Active Learning with Semi-Supervised Learning for automatic classification

A major problem in cybersecurity research is the correct labeling of up-to-date datasets. It relies on the availability of human experts, and is as such very cumbersome. Motivated by this, two techniques have been proposed for efficient labeling: Active Learning (AL) and Semi-Supervised Learning (SeSL). In this paper, we introduce Plusmine: an intrusion detection method that combines the benefits of AL and SeSL to efficiently automate classification. We develop new techniques for both components. Moreover, we empirically show that Plusmine obtains good and more robust results than benchmark methods

Detecting novel application layer cybervariants using supervised learning

Cyberdefense mechanisms such as Network Intrusion Detection Systems predominantly use signature-based approaches to effectively detect known malicious activities in network traffic. Unfortunately, constructing a database with signatures is very time-consuming and this approach can only find previously seen variants. Machine learning algorithms are known to be effective software tools in detecting known or unrelated novel intrusions, but if they are also able to detect unseen variants has not been studied. In this research, we study to what extent binary classification models are accurately able to detect novel variants of application layer targeted cyberattacks. To be more precise, we focus on detecting two types of intrusion variants, namely (Distributed) Denial-of-Service and Web attacks, targeting the Hypertext Transfer Protocol of a web server. We mathematically describe how two selected datasets are adjusted in three different experimental setups and the results of the classification models deployed in these setups are benchmarked using the Dutch Draw baseline method. The contributions of this research are as follows: we provide a procedure to create intrusion detection datasets combining information from the transport, network, and application layer to be directly used for machine learning purposes. We show that specific variants are successfully detected by these classification models trained to distinguish benign interactions from those of another variant. Despite this result, we demonstrate that the performances of the selected classifiers are not symmetric: the test score of a classifier trained on A and tested on B is not necessarily similar to the score of a classifier trained on B and tested on A. At last, we show that increasing the number of different variants in the training set does not necessarily lead to a higher detection rate of unseen variants. Selecting the right combination of a machine learning model with a (small) set of known intrusions included in the training data can result in a higher novel intrusion detection rate

Detecting Novel Variants of Application Layer (D)DoS Attacks using Supervised Learning

Denial of Service (DoS) attacks and their distributed variant (DDoS) are major digital threats in today’s cyberspace. Defense mechanisms such as Intrusion Detection Systems aim at finding these and other malicious activities in network traffic. They predominantly use signature-based approaches to effectively detect intrusions. Unfortunately, constructing a database with signatures is very time-consuming and this approach can only find previously seen variants. Machine learning algorithms are known to be effective tools in detecting intrusions, but it has not been studied if they are also able to detect unseen variants. In this research, we study to what extent supervised learning algorithms are able to detect novel variants of application layer (D)DoS attacks. To be more precise, we focus on detecting HTTP attacks targeting a web server. The contributions of this research are as follows: we provide a procedure to create intrusion detection datasets combining information from the transport, network, and application layer to be directly used for machine learning purposes. We show that specific (D)DoS variants are successfully detected by binary classifiers learned to distinguish benign entries from another (D)DoS attack. Despite this result, we demonstrate that the performance of a classifier trained on detecting variant A and tested on finding variant B is not necessarily similar to its performance when trained on B and tested on A. At last, we show that using more types of (D)DoS attacks in the training set does not necessarily lead to a higher detection rate of unseen variants. Thus, selecting the right combination of a machine learning model with a (small) set of intrusions included in the training data can result in a higher novel intrusion detection rate