125,018 research outputs found
A Family of Controllable Cellular Automata for Pseudorandom Number Generation
In this paper, we present a family of novel Pseudorandom Number Generators (PRNGs) based on Controllable Cellular Automata (CCA) ─ CCA0, CCA1, CCA2 (NCA), CCA3 (BCA), CCA4 (asymmetric NCA), CCA5, CCA6 and CCA7 PRNGs. The ENT and DIEHARD test suites are used to evaluate the randomness of these CCA PRNGs. The results show that their randomness is better than that of conventional CA and PCA PRNGs while they do not lose the structure simplicity of 1-d CA. Moreover, their randomness can be comparable to that of 2-d CA PRNGs. Furthermore, we integrate six different types of CCA PRNGs to form CCA PRNG groups to see if the randomness quality of such groups could exceed that of any individual CCA PRNG. Genetic Algorithm (GA) is used to evolve the configuration of the CCA PRNG groups. Randomness test results on the evolved CCA PRNG groups show that the randomness of the evolved groups is further improved compared with any individual CCA PRNG
Reinforcement Learning for the Unit Commitment Problem
In this work we solve the day-ahead unit commitment (UC) problem, by
formulating it as a Markov decision process (MDP) and finding a low-cost policy
for generation scheduling. We present two reinforcement learning algorithms,
and devise a third one. We compare our results to previous work that uses
simulated annealing (SA), and show a 27% improvement in operation costs, with
running time of 2.5 minutes (compared to 2.5 hours of existing
state-of-the-art).Comment: Accepted and presented in IEEE PES PowerTech, Eindhoven 2015, paper
ID 46273
Generation and sampling of quantum states of light in a silicon chip
Implementing large instances of quantum algorithms requires the processing of
many quantum information carriers in a hardware platform that supports the
integration of different components. While established semiconductor
fabrication processes can integrate many photonic components, the generation
and algorithmic processing of many photons has been a bottleneck in integrated
photonics. Here we report the on-chip generation and processing of quantum
states of light with up to eight photons in quantum sampling algorithms.
Switching between different optical pumping regimes, we implement the
Scattershot, Gaussian and standard boson sampling protocols in the same silicon
chip, which integrates linear and nonlinear photonic circuitry. We use these
results to benchmark a quantum algorithm for calculating molecular vibronic
spectra. Our techniques can be readily scaled for the on-chip implementation of
specialised quantum algorithms with tens of photons, pointing the way to
efficiency advantages over conventional computers
Statistical Classification of Cascading Failures in Power Grids
We introduce a new microscopic model of the outages in transmission power
grids. This model accounts for the automatic response of the grid to load
fluctuations that take place on the scale of minutes, when the optimum power
flow adjustments and load shedding controls are unavailable. We describe
extreme events, initiated by load fluctuations, which cause cascading failures
of loads, generators and lines. Our model is quasi-static in the causal,
discrete time and sequential resolution of individual failures. The model, in
its simplest realization based on the Directed Current description of the power
flow problem, is tested on three standard IEEE systems consisting of 30, 39 and
118 buses. Our statistical analysis suggests a straightforward classification
of cascading and islanding phases in terms of the ratios between average number
of removed loads, generators and links. The analysis also demonstrates
sensitivity to variations in line capacities. Future research challenges in
modeling and control of cascading outages over real-world power networks are
discussed.Comment: 8 pages, 8 figure
Pseudorandom number generation based on controllable cellular automata
A novel Cellular Automata (CA) Controllable CA (CCA) is proposed in this paper. Further, CCA are applied in Pseudorandom Number Generation. Randomness test results on CCA Pseudorandom Number Generators (PRNGs) show that they are better than 1-d CA PRNGs and can be comparable to 2-d ones. But they do not lose the structure simplicity of 1-d CA. Further, we develop several different types of CCA PRNGs. Based on the comparison of the randomness of different CCA PRNGs, we find that their properties are decided by the actions of the controllable cells and their neighbors. These novel CCA may be applied in other applications where structure non-uniformity or asymmetry is desired
A New Approach to Probabilistic Programming Inference
We introduce and demonstrate a new approach to inference in expressive
probabilistic programming languages based on particle Markov chain Monte Carlo.
Our approach is simple to implement and easy to parallelize. It applies to
Turing-complete probabilistic programming languages and supports accurate
inference in models that make use of complex control flow, including stochastic
recursion. It also includes primitives from Bayesian nonparametric statistics.
Our experiments show that this approach can be more efficient than previously
introduced single-site Metropolis-Hastings methods.Comment: Updated version of the 2014 AISTATS paper (to reflect changes in new
language syntax). 10 pages, 3 figures. Proceedings of the Seventeenth
International Conference on Artificial Intelligence and Statistics, JMLR
Workshop and Conference Proceedings, Vol 33, 201
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