160,026 research outputs found
On the Way to a Theory for Network Architectures
Abstract. The design of the future Internet is facing real challenges on network architecture. Attempts to resolve the issues related to naming/addressing, middle boxes, QoS-Security-Mobility interactions, cross-layer and inter-domain usually lead to endless debates. This is because the structure of the Internet has become too complex and has evolved with new functions added which are not always compatible with the existing functions. In order to analyze the architecture of the Internet in a strict manner, it is necessary to understand in depth the composition of functionalities within a protocol or between protocols. This paper presents a study on the composition of network functionalities and highlights future directions towards a theory for network architectures which includes the principles that network architectures should follow to ensure the normal operation of the member functions, detect all possible conflicts between them as well as figure out impossibilities
Neural Koopman prior for data assimilation
With the increasing availability of large scale datasets, computational power
and tools like automatic differentiation and expressive neural network
architectures, sequential data are now often treated in a data-driven way, with
a dynamical model trained from the observation data. While neural networks are
often seen as uninterpretable black-box architectures, they can still benefit
from physical priors on the data and from mathematical knowledge. In this
paper, we use a neural network architecture which leverages the long-known
Koopman operator theory to embed dynamical systems in latent spaces where their
dynamics can be described linearly, enabling a number of appealing features. We
introduce methods that enable to train such a model for long-term continuous
reconstruction, even in difficult contexts where the data comes in
irregularly-sampled time series. The potential for self-supervised learning is
also demonstrated, as we show the promising use of trained dynamical models as
priors for variational data assimilation techniques, with applications to e.g.
time series interpolation and forecasting
Report from the MPP Working Group to the NASA Associate Administrator for Space Science and Applications
NASA's Office of Space Science and Applications (OSSA) gave a select group of scientists the opportunity to test and implement their computational algorithms on the Massively Parallel Processor (MPP) located at Goddard Space Flight Center, beginning in late 1985. One year later, the Working Group presented its report, which addressed the following: algorithms, programming languages, architecture, programming environments, the way theory relates, and performance measured. The findings point to a number of demonstrated computational techniques for which the MPP architecture is ideally suited. For example, besides executing much faster on the MPP than on conventional computers, systolic VLSI simulation (where distances are short), lattice simulation, neural network simulation, and image problems were found to be easier to program on the MPP's architecture than on a CYBER 205 or even a VAX. The report also makes technical recommendations covering all aspects of MPP use, and recommendations concerning the future of the MPP and machines based on similar architectures, expansion of the Working Group, and study of the role of future parallel processors for space station, EOS, and the Great Observatories era
Towards a General Framework for Practical Quantum Network Protocols
The quantum internet is one of the frontiers of quantum information science. It will revolutionize the way we communicate and do other tasks, and it will allow for tasks that are not possible using the current, classical internet. The backbone of a quantum internet is entanglement distributed globally in order to allow for such novel applications to be performed over long distances. Experimental progress is currently being made to realize quantum networks on a small scale, but much theoretical work is still needed in order to understand how best to distribute entanglement and to guide the realization of large-scale quantum networks, and eventually the quantum internet, especially with the limitations of near-term quantum technologies. This work provides an initial step towards this goal. The main contribution of this thesis is a mathematical framework for entanglement distribution protocols in a quantum network, which allows for discovering optimal protocols using reinforcement learning. We start with a general development of quantum decision processes, which is the theoretical backdrop of reinforcement learning. Then, we define the general task of entanglement distribution in a quantum network, and we present ground- and satellite-based quantum network architectures that incorporate practical aspects of entanglement distribution. We combine the theory of decision processes and the practical quantum network architectures into an overall entanglement distribution protocol. We also define practical figures of merit to evaluate entanglement distribution protocols, which help to guide experimental implementations
Learning To Rank Diversely At Airbnb
Airbnb is a two-sided marketplace, bringing together hosts who own listings
for rent, with prospective guests from around the globe. Applying neural
network-based learning to rank techniques has led to significant improvements
in matching guests with hosts. These improvements in ranking were driven by a
core strategy: order the listings by their estimated booking probabilities,
then iterate on techniques to make these booking probability estimates more and
more accurate. Embedded implicitly in this strategy was an assumption that the
booking probability of a listing could be determined independently of other
listings in search results. In this paper we discuss how this assumption,
pervasive throughout the commonly-used learning to rank frameworks, is false.
We provide a theoretical foundation correcting this assumption, followed by
efficient neural network architectures based on the theory. Explicitly
accounting for possible similarities between listings, and reducing them to
diversify the search results generated strong positive impact. We discuss these
metric wins as part of the online A/B tests of the theory. Our method provides
a practical way to diversify search results for large-scale production ranking
systems.Comment: Search ranking, Diversity, e-commerc
Solving the discretised neutron diffusion equations using neural networks
This paper presents a new approach which uses the tools within artificial intelligence (AI) software libraries as an alternative way of solving partial differential equations (PDEs) that have been discretised using standard numerical methods. In particular, we describe how to represent numerical discretisations arising from the finite volume and finite element methods by pre-determining the weights of convolutional layers within a neural network. As the weights are defined by the discretisation scheme, no training of the network is required and the solutions obtained are identical (accounting for solver tolerances) to those obtained with standard codes often written in Fortran or C++. We also explain how to implement the Jacobi method and a multigrid solver using the functions available in AI libraries. For the latter, we use a U-Net architecture which is able to represent a sawtooth multigrid method. A benefit of using AI libraries in this way is that one can exploit their built-in technologies to enable the same code to run on different computer architectures (such as central processing units, graphics processing units or new-generation AI processors) without any modification. In this article, we apply the proposed approach to eigenvalue problems in reactor physics where neutron transport is described by diffusion theory. For a fuel assembly benchmark, we demonstrate that the solution obtained from our new approach is the same (accounting for solver tolerances) as that obtained from the same discretisation coded in a standard way using Fortran. We then proceed to solve a reactor core benchmark using the new approach. For both benchmarks we give timings for the neural network implementation run on a CPU and a GPU, and a serial Fortran code run on a CPU
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