525 research outputs found
On the Number of Membranes in Unary P Systems
We consider P systems with a linear membrane structure working on objects
over a unary alphabet using sets of rules resembling homomorphisms. Such a
restricted variant of P systems allows for a unique minimal representation of
the generated unary language and in that way for an effective solution of the
equivalence problem. Moreover, we examine the descriptional complexity of unary
P systems with respect to the number of membranes
Computing with cells: membrane systems - some complexity issues.
Membrane computing is a branch of natural computing which abstracts computing models from the structure and the functioning of the living cell. The main ingredients of membrane systems, called P systems, are (i) the membrane structure, which consists of a hierarchical arrangements of membranes which delimit compartments where (ii) multisets of symbols, called objects, evolve according to (iii) sets of rules which are localised and associated with compartments. By using the rules in a nondeterministic/deterministic maximally parallel manner, transitions between the system configurations can be obtained. A sequence of transitions is a computation of how the system is evolving. Various ways of controlling the transfer of objects from one membrane to another and applying the rules, as well as possibilities to dissolve, divide or create membranes have been studied. Membrane systems have a great potential for implementing massively concurrent systems in an efficient way that would allow us to solve currently intractable problems once future biotechnology gives way to a practical bio-realization. In this paper we survey some interesting and fundamental complexity issues such as universality vs. nonuniversality, determinism vs. nondeterminism, membrane and alphabet size hierarchies, characterizations of context-sensitive languages and other language classes and various notions of parallelism
Programs as Polypeptides
We describe a visual programming language for defining behaviors manifested
by reified actors in a 2D virtual world that can be compiled into programs
comprised of sequences of combinators that are themselves reified as actors.
This makes it possible to build programs that build programs from components of
a few fixed types delivered by diffusion using processes that resemble
chemistry as much as computation.Comment: in European Conference on Artificial Life (ECAL '15), York, UK, 201
Modeling Brain Circuitry over a Wide Range of Scales
If we are ever to unravel the mysteries of brain function at its most
fundamental level, we will need a precise understanding of how its component
neurons connect to each other. Electron Microscopes (EM) can now provide the
nanometer resolution that is needed to image synapses, and therefore
connections, while Light Microscopes (LM) see at the micrometer resolution
required to model the 3D structure of the dendritic network. Since both the
topology and the connection strength are integral parts of the brain's wiring
diagram, being able to combine these two modalities is critically important.
In fact, these microscopes now routinely produce high-resolution imagery in
such large quantities that the bottleneck becomes automated processing and
interpretation, which is needed for such data to be exploited to its full
potential. In this paper, we briefly review the Computer Vision techniques we
have developed at EPFL to address this need. They include delineating dendritic
arbors from LM imagery, segmenting organelles from EM, and combining the two
into a consistent representation
Simulating counting oracles with cooperation
We prove that monodirectional shallow chargeless P systems with active
membranes and minimal cooperation working in polynomial time precisely characterise
P#P
k , the complexity class of problems solved in polynomial time by deterministic
Turing machines with a polynomial number of parallel queries to an oracle for a counting
problem
Uniformity is weaker than semi-uniformity for some membrane systems
We investigate computing models that are presented as families of finite
computing devices with a uniformity condition on the entire family. Examples of
such models include Boolean circuits, membrane systems, DNA computers, chemical
reaction networks and tile assembly systems, and there are many others.
However, in such models there are actually two distinct kinds of uniformity
condition. The first is the most common and well-understood, where each input
length is mapped to a single computing device (e.g. a Boolean circuit) that
computes on the finite set of inputs of that length. The second, called
semi-uniformity, is where each input is mapped to a computing device for that
input (e.g. a circuit with the input encoded as constants). The former notion
is well-known and used in Boolean circuit complexity, while the latter notion
is frequently found in literature on nature-inspired computation from the past
20 years or so.
Are these two notions distinct? For many models it has been found that these
notions are in fact the same, in the sense that the choice of uniformity or
semi-uniformity leads to characterisations of the same complexity classes. In
other related work, we showed that these notions are actually distinct for
certain classes of Boolean circuits. Here, we give analogous results for
membrane systems by showing that certain classes of uniform membrane systems
are strictly weaker than the analogous semi-uniform classes. This solves a
known open problem in the theory of membrane systems. We then go on to present
results towards characterising the power of these semi-uniform and uniform
membrane models in terms of NL and languages reducible to the unary languages
in NL, respectively.Comment: 28 pages, 1 figur
Complete Problems for a Variant of P Systems with Active Membranes
We identify a family of decision problems that are hard for some complexity
classes defined in terms of P systems with active membranes working in polynomial time.
Furthermore, we prove the completeness of these problems in the case where the systems
are equipped with a form of priority that linearly orders their rules. Finally, we highlight
some possible connections with open problems related to the computational complexity
of P systems with active membranes
Beyond KernelBoost
In this Technical Report we propose a set of improvements with respect to the
KernelBoost classifier presented in [Becker et al., MICCAI 2013]. We start with
a scheme inspired by Auto-Context, but that is suitable in situations where the
lack of large training sets poses a potential problem of overfitting. The aim
is to capture the interactions between neighboring image pixels to better
regularize the boundaries of segmented regions. As in Auto-Context [Tu et al.,
PAMI 2009] the segmentation process is iterative and, at each iteration, the
segmentation results for the previous iterations are taken into account in
conjunction with the image itself. However, unlike in [Tu et al., PAMI 2009],
we organize our recursion so that the classifiers can progressively focus on
difficult-to-classify locations. This lets us exploit the power of the
decision-tree paradigm while avoiding over-fitting. In the context of this
architecture, KernelBoost represents a powerful building block due to its
ability to learn on the score maps coming from previous iterations. We first
introduce two important mechanisms to empower the KernelBoost classifier,
namely pooling and the clustering of positive samples based on the appearance
of the corresponding ground-truth. These operations significantly contribute to
increase the effectiveness of the system on biomedical images, where texture
plays a major role in the recognition of the different image components. We
then present some other techniques that can be easily integrated in the
KernelBoost framework to further improve the accuracy of the final
segmentation. We show extensive results on different medical image datasets,
including some multi-label tasks, on which our method is shown to outperform
state-of-the-art approaches. The resulting segmentations display high accuracy,
neat contours, and reduced noise
- …