347 research outputs found
COEL: A Web-based Chemistry Simulation Framework
The chemical reaction network (CRN) is a widely used formalism to describe
macroscopic behavior of chemical systems. Available tools for CRN modelling and
simulation require local access, installation, and often involve local file
storage, which is susceptible to loss, lacks searchable structure, and does not
support concurrency. Furthermore, simulations are often single-threaded, and
user interfaces are non-trivial to use. Therefore there are significant hurdles
to conducting efficient and collaborative chemical research. In this paper, we
introduce a new enterprise chemistry simulation framework, COEL, which
addresses these issues. COEL is the first web-based framework of its kind. A
visually pleasing and intuitive user interface, simulations that run on a large
computational grid, reliable database storage, and transactional services make
COEL ideal for collaborative research and education. COEL's most prominent
features include ODE-based simulations of chemical reaction networks and
multicompartment reaction networks, with rich options for user interactions
with those networks. COEL provides DNA-strand displacement transformations and
visualization (and is to our knowledge the first CRN framework to do so), GA
optimization of rate constants, expression validation, an application-wide
plotting engine, and SBML/Octave/Matlab export. We also present an overview of
the underlying software and technologies employed and describe the main
architectural decisions driving our development. COEL is available at
http://coel-sim.org for selected research teams only. We plan to provide a part
of COEL's functionality to the general public in the near future.Comment: 23 pages, 12 figures, 1 tabl
Delay Line as a Chemical Reaction Network
Chemistry as an unconventional computing medium presently lacks a systematic
approach to gather, store, and sort data over time. To build more complicated
systems in chemistries, the ability to look at data in the past would be a
valuable tool to perform complex calculations. In this paper we present the
first implementation of a chemical delay line providing information storage in
a chemistry that can reliably capture information over an extended period of
time. The delay line is capable of parallel operations in a single instruction,
multiple data (SIMD) fashion.
Using Michaelis-Menten kinetics, we describe the chemical delay line
implementation featuring an enzyme acting as a means to reduce copy errors. We
also discuss how information is randomly accessible from any element on the
delay line. Our work shows how the chemical delay line retains and provides a
value from a previous cycle. The system's modularity allows for integration
with existing chemical systems. We exemplify the delay line capabilities by
integration with a threshold asymmetric signal perceptron to demonstrate how it
learns all 14 linearly separable binary functions over a size two sliding
window. The delay line has applications in biomedical diagnosis and treatment,
such as smart drug delivery.Comment: 9 pages, 11 figures, 6 table
COEL: A Cloud-Based Reaction Network Simulator
Chemical Reaction Networks (CRNs) are a formalism to describe the macroscopic behavior of chemical systems. We introduce COEL, a web- and cloud-based CRN simulation framework, which does not require a local installation, runs simulations on a large computational grid, provides reliable database storage, and offers a visually pleasing and intuitive user interface. We present an overview of the underlying software, the technologies, and the main architectural approaches employed. Some of COEL’s key features include ODE-based simulations of CRNs and multicompartment reaction networks with rich interaction options, a built-in plotting engine, automatic DNA-strand displacement transformation and visualization, SBML/Octave/Matlab export, and a built-in genetic-algorithm- based optimization toolbox for rate constants. COEL is an open-source project hosted on GitHub (doi:10.5281/zenodo.46544), which allows interested research groups to deploy it on their own sever. Regular users can simply use the web instance at no cost at coel-sim.org. The framework is ideally suited for a collaborative use in both research and education
A Comparative Study of Reservoir Computing for Temporal Signal Processing
Reservoir computing (RC) is a novel approach to time series prediction using
recurrent neural networks. In RC, an input signal perturbs the intrinsic
dynamics of a medium called a reservoir. A readout layer is then trained to
reconstruct a target output from the reservoir's state. The multitude of RC
architectures and evaluation metrics poses a challenge to both practitioners
and theorists who study the task-solving performance and computational power of
RC. In addition, in contrast to traditional computation models, the reservoir
is a dynamical system in which computation and memory are inseparable, and
therefore hard to analyze. Here, we compare echo state networks (ESN), a
popular RC architecture, with tapped-delay lines (DL) and nonlinear
autoregressive exogenous (NARX) networks, which we use to model systems with
limited computation and limited memory respectively. We compare the performance
of the three systems while computing three common benchmark time series:
H{\'e}non Map, NARMA10, and NARMA20. We find that the role of the reservoir in
the reservoir computing paradigm goes beyond providing a memory of the past
inputs. The DL and the NARX network have higher memorization capability, but
fall short of the generalization power of the ESN
Improving PARMA Trailing
Taylor introduced a variable binding scheme for logic variables in his PARMA
system, that uses cycles of bindings rather than the linear chains of bindings
used in the standard WAM representation. Both the HAL and dProlog languages
make use of the PARMA representation in their Herbrand constraint solvers.
Unfortunately, PARMA's trailing scheme is considerably more expensive in both
time and space consumption. The aim of this paper is to present several
techniques that lower the cost.
First, we introduce a trailing analysis for HAL using the classic PARMA
trailing scheme that detects and eliminates unnecessary trailings. The
analysis, whose accuracy comes from HAL's determinism and mode declarations,
has been integrated in the HAL compiler and is shown to produce space
improvements as well as speed improvements. Second, we explain how to modify
the classic PARMA trailing scheme to halve its trailing cost. This technique is
illustrated and evaluated both in the context of dProlog and HAL. Finally, we
explain the modifications needed by the trailing analysis in order to be
combined with our modified PARMA trailing scheme. Empirical evidence shows that
the combination is more effective than any of the techniques when used in
isolation.
To appear in Theory and Practice of Logic Programming.Comment: 36 pages, 7 figures, 8 table
The use of image processing to determine cell defects in polycrystalline solar modules
This research aims to use image processingtodetermine cell defects in polycrystalline solar modules. Image processing is a process of enhancing images for differentapplications. One domain that seems to not yet utilise the use of image processing, is photovoltaics. An increased use of fossil fuels is damaging the earth and a call to protect the earth has resulted in the emergence of pollutant-free technologies such as polycrystalline photovoltaic (PV) cells, which are connected to make up solar modules. However, defects often affect the performance of PV cells and consequently solar modules. Electroluminescence (EL) images are used to examine polycrystalline solar (PV) modules to determine if the modules are defective. The main research question that this research addressed is“How can an image processing technique be used to effectively identify defective polycrystalline PV cells from EL images of such cells?“. The experimental research methodology was used to address the main research question. The initial investigation into the problem revealed that certain sectors within industry, as well as the Physics Department at Nelson Mandela University(NMU), do not currently utiliseimage processing when examining EL images of solar modules. The current process is a tedious, manual process whereby solar modules are manually inspected. An analysis of the current processes enabled the identification of ways in which to automatically examine EL images of solar modules. An analysis of literatureprovided a better understanding of the different techniques that are used to examine solar modules, and it was identified how image processing can be applied to EL images. Further analysis of literatureprovided a better understanding of image processing and how image classification experiments using Deep Learning (DL) as an image processing technique can be used to address the main research question. The outcome of the experiments conducted in this research weredifferentadaptive models(LeNet, MobileNet, Xception)that can classify EL images of PV cellsaccording to known standardsused by the Physics Department at NMU. The known standards yielded four classes; normal, uncritical, critical and very critical, which were used for the classification of EL images of PV cells. The adaptive models were evaluated to obtain the precision, recall and F1–scoreof the models.The precession, recall, and F1–score were required to determine how effective the models were in identifying defective PV cells from EL images.The results indicated that an image processing technique canbe used to identify defective polycrystalline PV cells from EL images of such cells. However, further research needs to be conducted to improve the effectiveness of the adaptive models
Administratief procesrecht in vergelijkend perspectief:Een rechtsvergelijkende studie naar de invloed van de functie van het beroep op de rechter bij de regeling van het administratieve procesrecht
Shift-Symmetric Configurations in Two-Dimensional Cellular Automata: Irreversibility, Insolvability, and Enumeration
The search for symmetry as an unusual yet profoundly appealing phenomenon,
and the origin of regular, repeating configuration patterns have long been a
central focus of complexity science and physics. To better grasp and understand
symmetry of configurations in decentralized toroidal architectures, we employ
group-theoretic methods, which allow us to identify and enumerate these inputs,
and argue about irreversible system behaviors with undesired effects on many
computational problems. The concept of so-called configuration shift-symmetry
is applied to two-dimensional cellular automata as an ideal model of
computation. Regardless of the transition function, the results show the
universal insolvability of crucial distributed tasks, such as leader election,
pattern recognition, hashing, and encryption. By using compact enumeration
formulas and bounding the number of shift-symmetric configurations for a given
lattice size, we efficiently calculate the probability of a configuration being
shift-symmetric for a uniform or density-uniform distribution. Further, we
devise an algorithm detecting the presence of shift-symmetry in a
configuration.
Given the resource constraints, the enumeration and probability formulas can
directly help to lower the minimal expected error and provide recommendations
for system's size and initialization. Besides cellular automata, the
shift-symmetry analysis can be used to study the non-linear behavior in various
synchronous rule-based systems that include inference engines, Boolean
networks, neural networks, and systolic arrays.Comment: 22 pages, 9 figures, 2 appendice
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