Two and a half hours of documentary footage from June 21, 2019, proceedings of the Senate when Bill C-81 received Royal Assent. The footage takes viewers behind the scenes with individuals closely involved with ensuring the Act received Royal Assent and features interviews with the Honourable Carla Qualtrough, Senator Jim Munson, James van Raalte, Sinead Tuite, Bill Adair, and Frank Folino.
Anomaly detection involves identifying observations that deviate from the normal behavior of a system. One of the ways to achieve this is by identifying the phenomena that characterize "normal" observations. Subsequently, based on the characteristics of data learned from the normal observations, new observations are classified as being either normal or not. Most state-of-the-art approaches, especially those which belong to the family parameterized statistical schemes, work under the assumption that the underlying distributions of the observations are stationary. That is, they assume that the distributions that are learned during the training (or learning) phase, though unknown, are not time-varying. They further assume that the same distributions are relevant even as new observations are encountered. Although such a " stationarity" assumption is relevant for many applications, there are some anomaly detection problems where stationarity cannot be assumed. For example, in network monitoring, the patterns which are learned to represent normal behavior may change over time due to several factors such as network infrastructure expansion, new services, growth of user population, etc. Similarly, in meteorology, identifying anomalous temperature patterns involves taking into account seasonal changes of normal observations. Detecting anomalies or outliers under these circumstances introduces several challenges. Indeed, the ability to adapt to changes in non-stationary environments is necessary so that anomalous observations can be identified even with changes in what would otherwise be classified as normal behavior. In this paper, we proposed to apply weak estimation theory for anomaly detection in dynamic environments. In particular, we apply this theory to detect anomaly activities in system calls. Our experimental results demonstrate that our proposal is both feasible and effective for the detection of such anomalous activities.
Samples of synthetic fused silica have been implanted at room temperature with silicon ions of energy 1.5 MeV. Fluences ranged from 1011 to 1013 cm−2. Samples were probed using variable‐energy positron annihilation spectroscopy. The Doppler‐broadening S parameter corresponding to the implanted region decreased with increasing fluence and saturated at a fluence of 1013 cm−2. It is shown that the decrease in the S parameter is due to the suppression of positronium (Ps) which is formed in the preimplanted material, due to the competing process of implantation‐induced trapping of positrons. In order to satisfactorily model the positron data it was necessary to account for positron trapping due to defects created by both electronic and nuclear stopping of the implanted ions. Annealing of the 1013 cm−2 sample resulted in measurable recovery of the preimplanted S parameter spectrum at 350 °C and complete recovery to the preimplanted condition at 600 °C. Volume compaction was also observed afterimplantation. Upon annealing, the compaction was seen to decrease by 75%.
Photobleaching of optical absorption bands in the 5 eV region and the creation of others at higher and lower energy have been examined in the case of ArF (6.4 eV) and KrF (5 eV) excimer laserirradiation of 3GeO2:97SiO2glasses. We report a difference in the transformation process of the neutral oxygen monovacancy and also of the germanium lone pair center (GLPC) into electron trap centers associated with fourfold coordinated Ge ions and Ge-E′ centers when we use one or the other laser. Correlations between absorption bands and electron spin resonance signals were made after different steps of laser irradiation. It was found that the KrF laser generates twice as many Ge-E′ centers as the ArF laser for the same dose of energy delivered. The main reason for this difference is found to be the more efficient bleaching of the GLPC (5.14 eV) by the KrF laser compared to that by the ArF laser.
A photolithographic method is described for fabricating refractive index Bragg gratings in photosensitive optical fiber by using a special phase mask grating made of silica glass. A KrF excimer laser beam (249 nm) at normal incidence is modulated spatially by the phase mask grating. The diffracted light, which forms a periodic, high-contrast intensity pattern with half the phase mask grating pitch, photoimprints a refractive index modulation into the core of photosensitive fiber placed behind, in proximity, and parallel, to the mask; the phase mask grating striations are oriented normal to the fiber axis. This method of fabricating in-fiber Bragg gratings is flexible, simple to use, results in reduced mechanical sensitivity of the grating writing apparatus and is functional even with low spatial and temporal coherence laser sources.
Silica-based thin-film multilayers are investigated as a means to enhance the effective second-order nonlinearity induced in silica glass structures by corona poling. Structures consisting of phosphorus-doped and undoped silica glass layers exhibit second harmonic generation (SHG) that is higher by an order of magnitude compared to the SHG in bulk silica glass poled under the same conditions. When the poled structure consists of two multilayered stacks separated in space, the stacks exhibit comparable poling-induced nonlinearities. This result suggests that the poling voltage is divided between the two stacks such that simultaneous poling of multiple regions within the sample is realized.