1,236 research outputs found
Discussion of the bacillus funcicularius n.sp., and a few remarks about the gallionella ferruginea ehrenberg
Identification of new bacteria strain, bacillus funicularius n.sp
Teaching photonic integrated circuits with Jupyter notebooks : design, simulation, fabrication
At Ghent University, we have built a course curriculum on integrated photonics, and in particular silicon photonics, based on interactive Jupyter Notebooks. This has been used in short workshops, specialization courses at PhD level, as well as the M.Sc. Photonics Engineering program at Ghent University and the Free University of Brussels. The course material teaches the concepts of on-chip waveguides, basic building blocks, circuits, the design process, fabrication and measurements. The Jupyter notebook environment provides an interface where static didactic content (text, figures, movies, formulas) is mixed with Python code that the user can modify and execute, and interactive plots and widgets to explore the effect of changes in circuits or components. The Python environment supplies a host of scientific and engineering libraries, while the photonic capabilities are based on IPKISS, a commercial design framework for photonic integrated circuits by Luceda Photonics. The IPKISS framework allows scripting of layout and simulation directly from the Jupyter notebooks, so the teaching modules contain live circuit simulation, as well as integration with electromagnetic solvers. Because this is a complete design framework, students can also use it to tape out a small chip design which is fabricated through a rapid prototyping service and then measured, allowing the students to validate the actual performance of their design against the original simulation. The scripting in Jupyter notebooks also provides a self-documenting design flow, and the use of an established design tool guarantees that the acquired skills can be transferred to larger, real-world design projects
Deploying Jupyter Notebooks at scale on XSEDE resources for Science Gateways and workshops
Jupyter Notebooks have become a mainstream tool for interactive computing in
every field of science. Jupyter Notebooks are suitable as companion
applications for Science Gateways, providing more flexibility and
post-processing capability to the users. Moreover they are often used in
training events and workshops to provide immediate access to a pre-configured
interactive computing environment. The Jupyter team released the JupyterHub web
application to provide a platform where multiple users can login and access a
Jupyter Notebook environment. When the number of users and memory requirements
are low, it is easy to setup JupyterHub on a single server. However, setup
becomes more complicated when we need to serve Jupyter Notebooks at scale to
tens or hundreds of users. In this paper we will present three strategies for
deploying JupyterHub at scale on XSEDE resources. All options share the
deployment of JupyterHub on a Virtual Machine on XSEDE Jetstream. In the first
scenario, JupyterHub connects to a supercomputer and launches a single node job
on behalf of each user and proxies back the Notebook from the computing node
back to the user's browser. In the second scenario, implemented in the context
of a XSEDE consultation for the IRIS consortium for Seismology, we deploy
Docker in Swarm mode to coordinate many XSEDE Jetstream virtual machines to
provide Notebooks with persistent storage and quota. In the last scenario we
install the Kubernetes containers orchestration framework on Jetstream to
provide a fault-tolerant JupyterHub deployment with a distributed filesystem
and capability to scale to thousands of users. In the conclusion section we
provide a link to step-by-step tutorials complete with all the necessary
commands and configuration files to replicate these deployments.Comment: 7 pages, 3 figures, PEARC '18: Practice and Experience in Advanced
Research Computing, July 22--26, 2018, Pittsburgh, PA, US
Jupyter in Computational Science
The articles in this special section discusses the applications supported by the Jupyter Notebook. Before notebooks, a scientist working with Python code, for instance, might have used a mixture of script files and code typed into an interactive shell. The shell is good for rapid experimentation, but the code and results are typically transient, and a linear record of everything that was tried would be long and not very clear. The notebook interface combines the convenience of the shell with some of the benefits of saving and editing code in a file, while also incorporating results, including rich output, such as plots, in a document that can be shared with others. The Jupyter Notebook is used through a web browser. Although it is often run locally, on a desktop or a laptop, this design means that it can also be used remotely, so the computation occurs, and the notebook files are saved, on an institutional server, a high-performance computing facility or in the clo
Thermal stability and topological protection of skyrmions in nanotracks
Magnetic skyrmions are hailed as a potential technology for data storage and
other data processing devices. However, their stability against thermal
fluctuations is an open question that must be answered before skyrmion-based
devices can be designed. In this work, we study paths in the energy landscape
via which the transition between the skyrmion and the uniform state can occur
in interfacial Dzyaloshinskii-Moriya finite-sized systems. We find three
mechanisms the system can take in the process of skyrmion nucleation or
destruction and identify that the transition facilitated by the boundary has a
significantly lower energy barrier than the other energy paths. This clearly
demonstrates the lack of the skyrmion topological protection in finite-sized
magnetic systems. Overall, the energy barriers of the system under
investigation are too small for storage applications at room temperature, but
research into device materials, geometry and design may be able to address
this
Accelerated Modeling of Near and Far-Field Diffraction for Coronagraphic Optical Systems
Accurately predicting the performance of coronagraphs and tolerancing optical
surfaces for high-contrast imaging requires a detailed accounting of
diffraction effects. Unlike simple Fraunhofer diffraction modeling, near and
far-field diffraction effects, such as the Talbot effect, are captured by
plane-to-plane propagation using Fresnel and angular spectrum propagation. This
approach requires a sequence of computationally intensive Fourier transforms
and quadratic phase functions, which limit the design and aberration
sensitivity parameter space which can be explored at high-fidelity in the
course of coronagraph design. This study presents the results of optimizing the
multi-surface propagation module of the open source Physical Optics Propagation
in PYthon (POPPY) package. This optimization was performed by implementing and
benchmarking Fourier transforms and array operations on graphics processing
units, as well as optimizing multithreaded numerical calculations using the
NumExpr python library where appropriate, to speed the end-to-end simulation of
observatory and coronagraph optical systems. Using realistic systems, this
study demonstrates a greater than five-fold decrease in wall-clock runtime over
POPPY's previous implementation and describes opportunities for further
improvements in diffraction modeling performance.Comment: Presented at SPIE ASTI 2018, Austin Texas. 11 pages, 6 figure
ClaimChain: Improving the Security and Privacy of In-band Key Distribution for Messaging
The social demand for email end-to-end encryption is barely supported by
mainstream service providers. Autocrypt is a new community-driven open
specification for e-mail encryption that attempts to respond to this demand. In
Autocrypt the encryption keys are attached directly to messages, and thus the
encryption can be implemented by email clients without any collaboration of the
providers. The decentralized nature of this in-band key distribution, however,
makes it prone to man-in-the-middle attacks and can leak the social graph of
users. To address this problem we introduce ClaimChain, a cryptographic
construction for privacy-preserving authentication of public keys. Users store
claims about their identities and keys, as well as their beliefs about others,
in ClaimChains. These chains form authenticated decentralized repositories that
enable users to prove the authenticity of both their keys and the keys of their
contacts. ClaimChains are encrypted, and therefore protect the stored
information, such as keys and contact identities, from prying eyes. At the same
time, ClaimChain implements mechanisms to provide strong non-equivocation
properties, discouraging malicious actors from distributing conflicting or
inauthentic claims. We implemented ClaimChain and we show that it offers
reasonable performance, low overhead, and authenticity guarantees.Comment: Appears in 2018 Workshop on Privacy in the Electronic Society
(WPES'18
Knowledge-base black holes: the next (small) big thing?
Hydraulic EngineeringCivil Engineering and Geoscience
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