1,874 research outputs found
AI Methods in Algorithmic Composition: A Comprehensive Survey
Algorithmic composition is the partial or total automation of the process of music composition
by using computers. Since the 1950s, different computational techniques related to
Artificial Intelligence have been used for algorithmic composition, including grammatical
representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint
programming and evolutionary algorithms. This survey aims to be a comprehensive
account of research on algorithmic composition, presenting a thorough view of the field for
researchers in Artificial Intelligence.This study was partially supported by a grant for the MELOMICS project
(IPT-300000-2010-010) from the Spanish Ministerio de Ciencia e InnovaciĂłn, and a grant for
the CAUCE project (TSI-090302-2011-8) from the Spanish Ministerio de Industria, Turismo
y Comercio. The first author was supported by a grant for the GENEX project (P09-TIC-
5123) from the ConsejerĂa de InnovaciĂłn y Ciencia de AndalucĂa
Behavior finding: Morphogenetic Designs Shaped by Function
Evolution has shaped an incredible diversity of multicellular living organisms, whose complex forms are self-made through a robust developmental process. This fundamental combination of biological evolution and development has served as an inspiration for novel engineering design methodologies, with the goal to overcome the scalability problems suffered by classical top-down approaches. Top-down methodologies are based on the manual decomposition of the design into modular, independent subunits. In contrast, recent computational morphogenetic techniques have shown that they were able to automatically generate truly complex innovative designs. Algorithms based on evolutionary computation and artificial development have been proposed to automatically design both the structures, within certain constraints, and the controllers that optimize their function. However, the driving force of biological evolution does not resemble an enumeration of design requirements, but much rather relies on the interaction of organisms within the environment. Similarly, controllers do not evolve nor develop separately, but are woven into the organism’s morphology. In this chapter, we discuss evolutionary morphogenetic algorithms inspired by these important aspects of biological evolution. The proposed methodologies could contribute to the automation of processes that design “organic” structures, whose morphologies and controllers are intended to solve a functional problem. The performance of the algorithms is tested on a class of optimization problems that we call behavior-finding. These challenges are not explicitly based on morphology or controller constraints, but only on the solving abilities and efficacy of the design. Our results show that morphogenetic algorithms are well suited to behavior-finding
Complex and Diverse Morphologies Can Develop from a Minimal Genomic Model
While development plays a critical role in the emergence
of diversity, its mechanical and chemical actions are considered
to be inextricably correlated with genetic control. Since
in most extant species the complex growth from zygote to
adult organism is orchestrated by a complex gene regulatory
network (GRN), the prevalent view is that the evolution
of diverse morphologies must result from the evolution
of diverse GRN topologies. By contrast, this work focuses
on the unique e ect of developmental processes through an
abstract model of self-regulated structure without genetic
regulation|only modulation of initial conditions. Here,
morphologies are generated by a simple evolutionary algorithm
searching for the longest instances of unfolding dynamics
based on tensegrity graphs. The usual regulatory
function of the genome is taken over by physical constraints
in the graphs, making morphological diversity a pure product
of structural complexi cation. By highlighting the potential
of structural development, our model is relevant to
both "structuralist" biological models and bio-inspired systems
engineering.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
The Evolution of Controller-Free Molecular Motors from Spatial Constraints
Locomotion of robotic and virtual agents is a
challenging task requiring the control of several degrees of
freedom as well as the coordination of multiple subsystems.
Traditionally, it is engineered by top-down design and finetuning
of the agent’s morphology and controller. A relatively
recent trend in fields such as evolutionary robotics, computer
animation and artificial life has been the coevolution and mutual
adaptation of the morphology and controller in computational
agent models. However, the controller is generally modeled as a
complex system, often a neural or gene regulatory network. In the
present study, inspired by molecular biology and based on normal
modal analysis, we formulate a behavior-finding framework for
the design of bipedal agents that are able to walk along a
filament and have no explicit control system. Instead, agents
interact with their environment in a purely reactive way. A simple
mutation operator, based on physical relaxation, is used to drive
the evolutionary search. Results show that gait patterns can be
evolutionarily engineered from the spatial interaction between
precisely tuned morphologies and the environment.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
Melomics: A Case-Study of AI in Spain
Traditionally focused on good old-fashioned
AI and robotics, the Spanish AI community
holds a vigorous computational intelligence
substrate. Neuromorphic, evolutionary, or
fuzzylike systems have been developed by many
research groups in the Spanish computer sciences.
It is no surprise, then, that these naturegrounded
efforts start to emerge, enriching the
AI catalogue of research projects and publications
and, eventually, leading to new directions
of basic or applied research. In this article, we
review the contribution of Melomics in computational
creativity.The work on Iamus was partially supported by grants
IPT-300000-2010-010 from the Spanish Ministerio de
Ciencia e InnovaciĂłn and TSI-090302-2011-8 from
the Spanish Ministerio de Industria, Turismo y Comercio.
The first and fourth authors were supported by
grant P09-TIC-5123 from the ConsejerĂa de InnovaciĂłn
y Ciencia, Junta de AndalucĂa
Just-In-Time eTraining Applied To Emergency Medical Services
While the applications of just-in-time training are more and
more spread, the ubiquitous mobile technology has not found
practical uses of this training strategy. As an original example
of services for healthcare, we present in this work an
application of eTraining that makes use of mobile telephones
to transmit medical and on-site information content to
emergency medical personnel that attend and emergency. The
state-of-the-art in related technologies, overall architecture, and
functioning of JITTER (for Just-In-Time Training for
Emergency Responders) is described in this work.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech. This work has been funded by the FIT-350100-2006-400
PROFIT project of the Spanish Ministerio de Industria,
Turismo y Comercio, American NSF grant DMI-0239180,
NIEHS (National Institute for Environmental Health
Sciences) grant 1R41ES014793-01, BanDeMar Networks,
Inc., the healthcare company iSOFT Sanidad, S.A., and the
CITIC Technology Centre
LTMaker: a tool for semiautomatic reconstruction of the embryonic lineage tree from 4D-microscopy
Studies of animal development using a 4Dmicroscopy
system generate an immense
amount of image data. In order to properly
analyze the recorded embryogenesis, a
computer-aided systematic process of categorization
of cells from the image data
should be accomplished.
We present a software tool named LTMaker
for the systematic semiautomatic identification
of embryonic cells centers and also to
determine the underlying linage tree. The
program saves the generated data to a file
so that further analysis of the embryo can
be performed with external tools.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
A review on machine learning approaches and trends in drug discovery
Abstract: Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.Instituto de Salud Carlos III; PI17/01826Instituto de Salud Carlos III; PI17/01561Xunta de Galicia; Ref. ED431D 2017/16Xunta de Galicia; Ref. ED431D 2017/23Xunta de Galicia; Ref. ED431C 2018/4
Design of a case management model for people with chronic disease (Heart Failure and COPD). Phase I: modeling and identification of the main components of the intervention through their actors: patients and professionals (DELTA-ICE-PRO Study
Background
Chronic diseases account for nearly 60% of deaths around the world. The extent of this silent epidemic has not met determined responses in governments, policies or professionals in order to transform old Health Care Systems, configured for acute diseases. There is a large list of research about alternative models for people with chronic conditions, many of them with an advanced practice nurse as a key provider, as case management. But some methodological concerns raise, above all, the design of the intervention (intensity, frequency, components, etc).
Methods/Design
Objectives: General: To develop the first and second phases (theorization and modeling) for designing a multifaceted case-management intervention in people with chronic conditions (COPD and heart failure) and their caregivers. Specific aims: 1) To identify key events in people living with chronic disease and their relation with the Health Care System, from their point of view. 2) To know the coping mechanisms developed by patients and their caregivers along the story with the disease. 3) To know the information processing and its utilization in their interactions with health care providers. 4) To detect potential unmet needs and the ways deployed by patients and their caregivers to resolve them. 5) To obtain a description from patients and caregivers, about their itineraries along the Health Care System, in terms of continuity, accessibility and comprehensiveness of care. 6) To build up a list of promising case-management interventions in patients with Heart Failure and COPD with this information in order to frame it into theoretical models for its reproducibility and conceptualization. 7) To undergo this list to expert judgment to assess its feasibility and pertinence in the Andalusian Health Care. Design: Qualitative research with two phases: For the first five objectives, a qualitative technique with biographic stories will be developed and, for the remaining objectives, an expert consensus through Delphi technique, on the possible interventions yielded from the first phase. The study will be developed in the provinces of AlmerĂa, Málaga and Granada in the Southern Spain, from patients included in the Andalusian Health Care Service database with the diagnosis of COPD or Heart Failure, with the collaboration of case manager nurses and general practitioners for the assessment of their suitability to inclusion criteria. Patients and caregivers will be interviewed in their homes or their Health Centers, with their family or their case manager nurse as mediator.
Discussion
First of a series of studies intended to design a case-management service for people with heart failure and COPD, in the Andalusian Health Care System, where case management has been implemented since 2002. Accordingly with the steps of a theoretical model for complex interventions, in this study, theorization and intervention modeling phases will be developed.This research was carried out with the support of one research grant, awarded by the Regional Health Ministry of Andalusia (Exp. 0222/2008
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