Spectral Signatures in Backdoor Attacks

A recent line of work has uncovered a new form of data poisoning: so-called \emph{backdoor} attacks. These attacks are particularly dangerous because they do not affect a network's behavior on typical, benign data. Rather, the network only deviates from its expected output when triggered by a perturbation planted by an adversary. In this paper, we identify a new property of all known backdoor attacks, which we call \emph{spectral signatures}. This property allows us to utilize tools from robust statistics to thwart the attacks. We demonstrate the efficacy of these signatures in detecting and removing poisoned examples on real image sets and state of the art neural network architectures. We believe that understanding spectral signatures is a crucial first step towards designing ML systems secure against such backdoor attacksComment: 16 pages, accepted to NIPS 201

Stellar Double Coronagraph: a multistage coronagraphic platform at Palomar observatory

We present a new instrument, the "Stellar Double Coronagraph" (SDC), a flexible coronagraphic platform. Designed for Palomar Observatory's 200" Hale telescope, its two focal and pupil planes allow for a number of different observing configurations, including multiple vortex coronagraphs in series for improved contrast at small angles. We describe the motivation, design, observing modes, wavefront control approaches, data reduction pipeline, and early science results. We also discuss future directions for the instrument.Comment: 25 pages, 12 figures. Correspondence welcome. The published work is open access and differs trivially from the version posted here. The published version may be found at http://iopscience.iop.org/article/10.1088/1538-3873/128/965/075003/met

A parallel algorithm to calculate the costrank of a network

We developed analogous parallel algorithms to implement CostRank for distributed memory parallel computers using multi processors. Our intent is to make CostRank calculations for the growing number of hosts in a fast and a scalable way. In the same way we intent to secure large scale networks that require fast and reliable computing to calculate the ranking of enormous graphs with thousands of vertices (states) and millions or arcs (links). In our proposed approach we focus on a parallel CostRank computational architecture on a cluster of PCs networked via Gigabit Ethernet LAN to evaluate the performance and scalability of our implementation. In particular, a partitioning of input data, graph files, and ranking vectors with load balancing technique can improve the runtime and scalability of large-scale parallel computations. An application case study of analogous Cost Rank computation is presented. Applying parallel environment models for one-dimensional sparse matrix partitioning on a modified research page, results in a significant reduction in communication overhead and in per-iteration runtime. We provide an analytical discussion of analogous algorithms performance in terms of I/O and synchronization cost, as well as of memory usage