Modal Interferometers Based on a Tapered Special Photonic Crystal Fiber for Highly Sensitive Detection

The use of a tapered special photonic crystal fiber (PCF) with collapsed air holes in the waist (the thinnest part of a taper) for highly sensitive detection of strain, high temperature, and fast detection of hydrogen with concentrations between 1.2 and 5.6 vol.% and biosensing is demonstrated. In the tapered PCF, a fundamental core mode couples to a few modes of the solid taper waist. Owing to the beating between the waist modes, the transmission spectra of the tapered PCF exhibit several interference peaks, which are sensitive to refractive index changes of a medium that surrounds the taper and also to changes of a taper length. The changes can be visualized as a shift of the peaks in the output spectrum pattern

5PM: Secure Pattern Matching

In this paper we consider the problem of secure pattern matching that allows single-character wildcards and substring matching in the malicious (stand-alone) setting. Our protocol, called 5PM, is executed between two parties: Server, holding a text of length $n$, and Client, holding a pattern of length $m$ to be matched against the text, where our notion of matching is more general and includes non-binary alphabets, non-binary Hamming distance and non-binary substring matching. 5PM is the first secure expressive pattern matching protocol designed to optimize round complexity by carefully specifying the entire protocol round by round. In the malicious model, 5PM requires $O((m+n)k^2)$ bandwidth and $O(m+n)$ encryptions, where $m$ is the pattern length and $n$ is the text length. Further, 5PM can hide pattern size with no asymptotic additional costs in either computation or bandwidth. Finally, 5PM requires only two rounds of communication in the honest-but-curious model and eight rounds in the malicious model. Our techniques reduce pattern matching and generalized Hamming distance problems to a novel linear algebra formulation that allows for generic solutions based on any additively homomorphic encryption. We believe our efficient algebraic techniques are of independent interest

Effect of electron irradiation on the optical properties of bismuth doped hafnia-yttria-alumina-silicate fiber

We report a study on transformations in absorption and emission spectra of novel bismuth (Bi) doped hafnia-yttria-alumina-silicate fiber, which arise as the result of bombardment by high-energy (beta) electrons. Among the featuring data obtained, we reveal substantial growth of `active' Bi center content in the fiber core-glass with increasing beta-irradiation dosage, resulting in dose-dependent intensification of the resonant-absorption bands and enhancement of the emissive potential of the fiber in near-IR, inherent to these centers. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

A two teraflop swarm

© 2018 Jones, Studley, Hauert and Winfield. We introduce the Xpuck swarm, a research platform with an aggregate raw processing power in excess of two teraflops. The swarm uses 16 e-puck robots augmented with custom hardware that uses the substantial CPU and GPU processing power available from modern mobile system-on-chip devices. The augmented robots, called Xpucks, have at least an order of magnitude greater performance than previous swarm robotics platforms. The platform enables new experiments that require high individual robot computation and multiple robots. Uses include online evolution or learning of swarm controllers, simulation for answering what-if questions about possible actions, distributed super-computing for mobile platforms, and real-world applications of swarm robotics that requires image processing, or SLAM. The teraflop swarm could also be used to explore swarming in nature by providing platforms with similar computational power as simple insects. We demonstrate the computational capability of the swarm by implementing a fast physics-based robot simulator and using this within a distributed island model evolutionary system, all hosted on the Xpucks

Communication sparsity in distributed spiking neural network simulations to improve scalability

In the last decade there has been a surge in the number of big science projects interested in achieving a comprehensive understanding of the functions of the brain, using Spiking Neuronal Network (SNN) simulations to aid discovery and experimentation. Such an approach increases the computational demands on SNN simulators: if natural scale brain-size simulations are to be realized, it is necessary to use parallel and distributed models of computing. Communication is recognized as the dominant part of distributed SNN simulations. As the number of computational nodes increases, the proportion of time the simulation spends in useful computing (computational efficiency) is reduced and therefore applies a limit to scalability. This work targets the three phases of communication to improve overall computational efficiency in distributed simulations: implicit synchronization, process handshake and data exchange. We introduce a connectivity-aware allocation of neurons to compute nodes by modeling the SNN as a hypergraph. Partitioning the hypergraph to reduce interprocess communication increases the sparsity of the communication graph. We propose dynamic sparse exchange as an improvement over simple point-to-point exchange on sparse communications. Results show a combined gain when using hypergraph-based allocation and dynamic sparse communication, increasing computational efficiency by up to 40.8 percentage points and reducing simulation time by up to 73%. The findings are applicable to other distributed complex system simulations in which communication is modeled as a graph network