Publicly available scientific resources help establish evaluation standards, provide a platform for teaching and improve reproducibility. Version 4 of the Insight ToolKit (ITK4) seeks to establish new standards in publicly available image registration methodology. ITK4 makes several advances in comparison to previous versions of ITK. ITK4 supports both multivariate images and objective functions; it also unifies high-dimensional (deformation field) and low-dimensional (affine) transformations with metrics that are reusable across transform types and with composite transforms that allow arbitrary series of geometric mappings to be chained together seamlessly. Metrics and optimizers take advantage of multi-core resources, when available. Furthermore, ITK4 reduces the parameter optimization burden via principled heuristics that automatically set scaling across disparate parameter types (rotations vs. translations). A related approach also constrains steps sizes for gradient-based optimizers. The result is that tuning for different metrics and/or image pairs is rarely necessary allowing the researcher to more easily focus on design/comparison of registration strategies. In total, the ITK4 contribution is intended as a structure to support reproducible research practices, will provide a more extensive foundation against which to evaluate new work in image registration and also enable application level programmers a broad suite of tools on which to build. Finally, we contextualize this work with a reference registration evaluation study with application to pediatric brain labeling.1

We recognized that the envelope fluctuation and decay of seismic records carries ultra low-frequency (ULF, i.e., the frequency below the lowest frequency in the source spectrum) signals that can be used to estimate the long-wavelength velocity structure. We then developed envelope inversion for the recovery of low-wavenumber components of media (smooth background), so that the initial model dependence of waveform inversion can be reduced. We derived the misfit function and the corresponding gradient operator for envelope inversion. To understand the long-wavelength recovery by the envelope inversion, we developed a nonlinear seismic signal model, the modulation signal model, as the basis for retrieving the ULF data and studied the nonlinear scale separation by the envelope operator. To separate the envelope data from the wavefield data (envelope extraction), a demodulation operator (envelope operator) was applied to the waveform data. Numerical tests using synthetic data for the Marmousi model proved the validity and feasibility of the proposed approach. The final results of combined [Formula: see text] (envelope-inversion for smooth background plus waveform-inversion for high-resolution velocity structure) indicated that it can deliver much improved results compared with regular full-waveform inversion (FWI) alone. Furthermore, to test the independence of the envelope to the source frequency band, we used a low-cut source wavelet (cut from 5 Hz below) to generate the synthetic data. The envelope inversion and the combined [Formula: see text] showed no appreciable difference from the full-band source results. The proposed envelope inversion is also an efficient method with very little extra work compared with conventional FWI.

Advanced persistent threat (APT) is a serious threat to the Internet. With the aid of APT malware, attackers can remotely control infected machines and steal sensitive information. DNS is popular for malware to locate command and control (C&C) servers. In this paper, we propose a novel system placed at the network egress point that aims to efficiently and effectively detect APT malware infections based on malicious DNS and traffic analysis. The system uses malicious DNS analysis techniques to detect suspicious APT malware C&C domains, and then analyzes the traffic of the corresponding suspicious IP using the signature-based and anomaly based detection technology. We extracted 14 features based on big data to characterize different properties of malware-related DNS and the ways that they are queried, and we also defined network traffic features that can identify the traffic of compromised clients that have remotely been controlled. We built a reputation engine to compute a reputation score for an IP address using these features vector together. Our experiment was performed at a large local institute network for two months, and all the features were studied with big data, which includes ∼400 million DNS queries. Our security approach cannot only substantially reduce the volume of network traffic that needs to be recorded and analyzed but also improve the sustainability of the system. INDEX TERMSAPT, malware infections, DNS, intrusion detection.

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