On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ∼ 1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40 − 8 + 8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 M ⊙ . An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ∼ 40 Mpc ) less than 11 hours after the merger by the One-Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ∼10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ∼ 9 and ∼ 16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC 4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta.
Machine learning (ML) has become a core component of many real-world applications and training data is a key factor that drives current progress. This huge success has led Internet companies to deploy machine learning as a service (MLaaS). Recently, the first membership inference attack has shown that extraction of information on the training set is possible in such MLaaS settings, which has severe security and privacy implications.However, the early demonstrations of the feasibility of such attacks have many assumptions on the adversary, such as using multiple so-called shadow models, knowledge of the target model structure, and having a dataset from the same distribution as the target model's training data. We relax all these key assumptions, thereby showing that such attacks are very broadly applicable at low cost and thereby pose a more severe risk than previously thought. We present the most comprehensive study so far on this emerging and developing threat using eight diverse datasets which show the viability of the proposed attacks across domains.In addition, we propose the first effective defense mechanisms against such broader class of membership inference attacks that maintain a high level of utility of the ML model.
Previous detections of individual astrophysical sources of neutrinos are limited to the Sun and the supernova 1987A, whereas the origins of the diffuse flux of high-energy cosmic neutrinos remain unidentified. On 22 September 2017, we detected a high-energy neutrino, IceCube-170922A, with an energy of ~290 tera-electron volts. Its arrival direction was consistent with the location of a known γ-ray blazar, TXS 0506+056, observed to be in a flaring state. An extensive multiwavelength campaign followed, ranging from radio frequencies to γ-rays. These observations characterize the variability and energetics of the blazar and include the detection of TXS 0506+056 in very-high-energy γ-rays. This observation of a neutrino in spatial coincidence with a γ-ray-emitting blazar during an active phase suggests that blazars may be a source of high-energy neutrinos.
We report on the γ -ray activity of the high-synchrotron-peaked BL Lacertae object Markarian 421 (Mrk 421) during the first 1.5 years of Fermi operation, from 2008 August 5 to 2010 March 12. We find that the Large Area Telescope (LAT) γ -ray spectrum above 0.3 GeV can be well described by a power-law function with photon index Γ = 1.78 ± 0.02 and average photon flux F (>0.3 GeV) = (7.23 ± 0.16) × 10 −8 ph cm −2 s −1 . Over this time period, the Fermi-LAT spectrum above 0.3 GeV was evaluated on seven-day-long time intervals, showing significant variations in the photon flux (up to a factor ∼3 from the minimum to the maximum flux) but mild spectral variations. The variability amplitude at X-ray frequencies measured by RXTE/ASM and Swift/BAT is substantially larger than that in γ -rays measured by Fermi-LAT, and these two energy ranges are not significantly correlated. We also present the first results from the 4.5 month long multifrequency campaign on Mrk 421, which included the VLBA, Swift, RXTE, MAGIC, the F-GAMMA, GASP-WEBT, and other collaborations and instruments that provided excellent temporal and energy coverage of the source throughout the entire campaign During this campaign, Mrk 421 showed a low activity at all wavebands. The extensive multi-instrument (radio to TeV) data set provides an unprecedented, complete look at the quiescent spectral energy distribution (SED) for this source. The broadband SED was reproduced with a leptonic (one-zone synchrotron self-Compton) and a hadronic model (synchrotron proton blazar). Both frameworks are able to describe the average SED reasonably well, implying comparable jet powers but very different characteristics for the blazar emission site.
Machine Learning (ML) models are applied in a variety of tasks such as network intrusion detection or malware classification. Yet, these models are vulnerable to a class of malicious inputs known as adversarial examples. These are slightly perturbed inputs that are classified incorrectly by the ML model. The mitigation of these adversarial inputs remains an open problem.As a step towards understanding adversarial examples, we show that they are not drawn from the same distribution than the original data, and can thus be detected using statistical tests. Using this knowledge, we introduce a complimentary approach to identify specific inputs that are adversarial. Specifically, we augment our ML model with an additional output, in which the model is trained to classify all adversarial inputs.We evaluate our approach 1 on multiple adversarial example crafting methods (including the fast gradient sign and saliency map methods) with several datasets. The statistical test flags sample sets containing adversarial inputs confidently at sample sizes between 10 and 100 data points. Furthermore, our augmented model either detects adversarial examples as outliers with high accuracy (> 80%) or increases the adversary's cost-the perturbation added-by more than 150%. In this way, we show that statistical properties of adversarial examples are essential to their detection. 2
Information-flow analysis is a powerful technique for reasoning about the sensitive information exposed by a program during its execution. We present the first automatic method for information-flow analysis that discovers what information is leaked and computes its comprehensive quantitative interpretation. The leaked information is characterized by an equivalence relation on secret artifacts, and is represented by a logical assertion over the corresponding program variables. Our measurement procedure computes the number of discovered equivalence classes and their sizes. This provides a basis for computing a set of quantitative properties, which includes all established information-theoretic measures in quantitative information-flow. Our method exploits an inherent connection between formal models of qualitative information-flow and program verification techniques. We provide an implementation of our method that builds upon existing tools for program verification and informationtheoretic analysis. Our experimental evaluation indicates the practical applicability of the presented method.
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