13 research outputs found
Data Science and Ebola
Data Science---Today, everybody and everything produces data. People produce
large amounts of data in social networks and in commercial transactions.
Medical, corporate, and government databases continue to grow. Sensors continue
to get cheaper and are increasingly connected, creating an Internet of Things,
and generating even more data. In every discipline, large, diverse, and rich
data sets are emerging, from astrophysics, to the life sciences, to the
behavioral sciences, to finance and commerce, to the humanities and to the
arts. In every discipline people want to organize, analyze, optimize and
understand their data to answer questions and to deepen insights. The science
that is transforming this ocean of data into a sea of knowledge is called data
science. This lecture will discuss how data science has changed the way in
which one of the most visible challenges to public health is handled, the 2014
Ebola outbreak in West Africa.Comment: Inaugural lecture Leiden Universit
First Forcer results on deep-inelastic scattering and related quantities
We present results on the fourth-order splitting functions and coefficient
functions obtained using Forcer, a four-loop generalization of the Mincer
program for the parametric reduction of self-energy integrals. We have computed
the respective lowest three even-N and odd-N moments for the non-singlet
splitting functions and the non-singlet coefficient functions in
electromagnetic and nu+nu(bar) charged-current deep-inelastic scattering, and
the N=2 and N=4 results for the corresponding flavour-singlet quantities.
Enough moments have been obtained for an LLL-based determination of the
analytic N-dependence of the nf^3 and nf^2 parts, respectively, of the singlet
and non-singlet splitting functions. The large-N limit of the latter provides
the complete nf^2 contributions to the four-loop cusp anomalous dimension. Our
results also provide additional evidence of a non-vanishing contribution of
quartic group invariants to the cusp anomalous dimension.Comment: 11 pages, LaTeX (PoS style), 4 eps-figures. To appear in the
proceedings of `Loops & Legs 2016', Leipzig (Germany), April 201
Simple, Parallel, High-Performance Virtual Machines for Extreme Computations
We introduce a high-performance virtual machine (VM) written in a numerically
fast language like Fortran or C to evaluate very large expressions. We discuss
the general concept of how to perform computations in terms of a VM and present
specifically a VM that is able to compute tree-level cross sections for any
number of external legs, given the corresponding byte code from the optimal
matrix element generator, O'Mega. Furthermore, this approach allows to
formulate the parallel computation of a single phase space point in a simple
and obvious way. We analyze hereby the scaling behaviour with multiple threads
as well as the benefits and drawbacks that are introduced with this method. Our
implementation of a VM can run faster than the corresponding native, compiled
code for certain processes and compilers, especially for very high
multiplicities, and has in general runtimes in the same order of magnitude. By
avoiding the tedious compile and link steps, which may fail for source code
files of gigabyte sizes, new processes or complex higher order corrections that
are currently out of reach could be evaluated with a VM given enough computing
power.Comment: 19 pages, 8 figure
Structured parallel programming for Monte Carlo Tree Search
The thesis is part of a bigger project, the HEPGAME (High Energy Physics Game). The main objective for HEPGAME is the utilization of AI solutions, particularly by using MCTS for simplification of HEP calculations. One of the issues is solving mathematical expressions of interest with millions of terms. These calculations can be solved with the FORM program, which is software for symbolic manipulation. Since these calculations are computationally intensive and take a large amount of time, the FORM program was parallelized to solve them in a reasonable amount of time.Therefore, any new algorithm based on MCTS, should also be parallelized. This requirement was behind the problem statement of the thesis: “How do we design a structured pattern-based parallel programming approach for efficient parallelism of MCTS for both multi-core and manycore shared-memory machines?”.To answer this question, the thesis approached the MCTS parallelization problem in three levels: (1) implementation level, (2) data structure level, and (3) algorithm level.In the implementation level, we proposed task-level parallelization over thread-level parallelization. Task-level parallelization provides us with efficient parallelism for MCTS to utilize cores on both multi-core and manycore machines.In the data structure level, we presented a lock-free data structure that guarantees the correctness. A lock-free data structure (1) removes the synchronization overhead when a parallel program needs many tasks to feed its cores and (2) improves both performance and scalability.In the algorithm level, we first explained how to use pipeline pattern for parallelization of MCTS to overcome search overhead. Then, through a step by step approach, we were able to propose and detail the structured parallel programming approach for Monte Carlo Tree Search.Algorithms and the Foundations of Software technolog
Data science and Ebola
Inaugural Lecture by Prof.dr. Aske Plaat on the acceptance of the position of professor of Data Science at the Universiteit Leiden on Monday 13 April 2015Algorithms and the Foundations of Software technolog