40,389 research outputs found
Process Mining of Programmable Logic Controllers: Input/Output Event Logs
This paper presents an approach to model an unknown Ladder Logic based
Programmable Logic Controller (PLC) program consisting of Boolean logic and
counters using Process Mining techniques. First, we tap the inputs and outputs
of a PLC to create a data flow log. Second, we propose a method to translate
the obtained data flow log to an event log suitable for Process Mining. In a
third step, we propose a hybrid Petri net (PN) and neural network approach to
approximate the logic of the actual underlying PLC program. We demonstrate the
applicability of our proposed approach on a case study with three simulated
scenarios
Fully Dynamic Connectivity in Amortized Expected Time
Dynamic connectivity is one of the most fundamental problems in dynamic graph
algorithms. We present a randomized Las Vegas dynamic connectivity data
structure with amortized expected update time and
worst case query time, which comes very close to the
cell probe lower bounds of Patrascu and Demaine (2006) and Patrascu and Thorup
(2011)
Instrument for determining coincidence and elapse time between independent sources of random sequential events
An instrument that receives pulses from a primary external source and one or more secondary external sources and determines when there is coincidence between the primary and one of the secondary sources is described. The instrument generates a finite time window (coincidence aperture) during which coincidence is defined to have occurred. The time intervals between coincidence apertures in which coincidences occur are measured
B\"uchi VASS recognise w-languages that are Sigma^1_1 - complete
This short note exhibits an example of a Sigma^1_1-complete language that can
be recognised by a one blind counter B\"uchi automaton (or equivalently a
B\"uchi VASS with only one place)
A Heterosynaptic Learning Rule for Neural Networks
In this article we intoduce a novel stochastic Hebb-like learning rule for
neural networks that is neurobiologically motivated. This learning rule
combines features of unsupervised (Hebbian) and supervised (reinforcement)
learning and is stochastic with respect to the selection of the time points
when a synapse is modified. Moreover, the learning rule does not only affect
the synapse between pre- and postsynaptic neuron, which is called homosynaptic
plasticity, but effects also further remote synapses of the pre- and
postsynaptic neuron. This more complex form of synaptic plasticity has recently
come under investigations in neurobiology and is called heterosynaptic
plasticity. We demonstrate that this learning rule is useful in training neural
networks by learning parity functions including the exclusive-or (XOR) mapping
in a multilayer feed-forward network. We find, that our stochastic learning
rule works well, even in the presence of noise. Importantly, the mean learning
time increases with the number of patterns to be learned polynomially,
indicating efficient learning.Comment: 19 page
The Power of Duples (in Self-Assembly): It's Not So Hip To Be Square
In this paper we define the Dupled abstract Tile Assembly Model (DaTAM),
which is a slight extension to the abstract Tile Assembly Model (aTAM) that
allows for not only the standard square tiles, but also "duple" tiles which are
rectangles pre-formed by the joining of two square tiles. We show that the
addition of duples allows for powerful behaviors of self-assembling systems at
temperature 1, meaning systems which exclude the requirement of cooperative
binding by tiles (i.e., the requirement that a tile must be able to bind to at
least 2 tiles in an existing assembly if it is to attach). Cooperative binding
is conjectured to be required in the standard aTAM for Turing universal
computation and the efficient self-assembly of shapes, but we show that in the
DaTAM these behaviors can in fact be exhibited at temperature 1. We then show
that the DaTAM doesn't provide asymptotic improvements over the aTAM in its
ability to efficiently build thin rectangles. Finally, we present a series of
results which prove that the temperature-2 aTAM and temperature-1 DaTAM have
mutually exclusive powers. That is, each is able to self-assemble shapes that
the other can't, and each has systems which cannot be simulated by the other.
Beyond being of purely theoretical interest, these results have practical
motivation as duples have already proven to be useful in laboratory
implementations of DNA-based tiles
- …