2,572 research outputs found
Coalescent simulation in continuous space:Algorithms for large neighbourhood size
Many species have an essentially continuous distribution in space, in which there are no natural divisions between randomly mating subpopulations. Yet, the standard approach to modelling these populations is to impose an arbitrary grid of demes, adjusting deme sizes and migration rates in an attempt to capture the important features of the population. Such indirect methods are required because of the failure of the classical models of isolation by distance, which have been shown to have major technical flaws. A recently introduced model of extinction and recolonisation in two dimensions solves these technical problems, and provides a rigorous technical foundation for the study of populations evolving in a spatial continuum. The coalescent process for this model is simply stated, but direct simulation is very inefficient for large neighbourhood sizes. We present efficient and exact algorithms to simulate this coalescent process for arbitrary sample sizes and numbers of loci, and analyse these algorithms in detail
Scale-invariance in gravity and implications for the cosmological constant
Recently a scale invariant theory of gravity was constructed by imposing a
conformal symmetry on general relativity. The imposition of this symmetry
changed the configuration space from superspace - the space of all Riemannian
3-metrics modulo diffeomorphisms - to conformal superspace - the space of all
Riemannian 3-metrics modulo diffeomorphisms and conformal transformations.
However, despite numerous attractive features, the theory suffers from at least
one major problem: the volume of the universe is no longer a dynamical
variable. In attempting to resolve this problem a new theory is found which has
several surprising and atractive features from both quantisation and
cosmological perspectives. Furthermore, it is an extremely restrictive theory
and thus may provide testable predictions quickly and easily. One particularly
interesting feature of the theory is the resolution of the cosmological
constant problem.Comment: Replaced with final version: minor changes to text; references adde
Scale-invariant gravity: Spacetime recovered
The configuration space of general relativity is superspace - the space of
all Riemannian 3-metrics modulo diffeomorphisms. However, it has been argued
that the configuration space for gravity should be conformal superspace - the
space of all Riemannian 3-metrics modulo diffeomorphisms and conformal
transformations. Recently a manifestly 3-dimensional theory was constructed
with conformal superspace as the configuration space. Here a fully
4-dimensional action is constructed so as to be invariant under conformal
transformations of the 4-metric using general relativity as a guide. This
action is then decomposed to a (3+1)-dimensional form and from this to its
Jacobi form. The surprising thing is that the new theory turns out to be
precisely the original 3-dimensional theory. The physical data is identified
and used to find the physical representation of the theory. In this
representation the theory is extremely similar to general relativity. The
clarity of the 4-dimensional picture should prove very useful for comparing the
theory with those aspects of general relativity which are usually treated in
the 4-dimensional framework.Comment: Replaced with final version: minor changes to tex
The physical gravitational degrees of freedom
When constructing general relativity (GR), Einstein required 4D general
covariance. In contrast, we derive GR (in the compact, without boundary case)
as a theory of evolving 3-dimensional conformal Riemannian geometries obtained
by imposing two general principles: 1) time is derived from change; 2) motion
and size are relative. We write down an explicit action based on them. We
obtain not only GR in the CMC gauge, in its Hamiltonian 3 + 1 reformulation but
also all the equations used in York's conformal technique for solving the
initial-value problem. This shows that the independent gravitational degrees of
freedom obtained by York do not arise from a gauge fixing but from hitherto
unrecognized fundamental symmetry principles. They can therefore be identified
as the long-sought Hamiltonian physical gravitational degrees of freedom.Comment: Replaced with published version (minor changes and added references
Deep Residual Policy Reinforcement Learning as a Corrective Term in Process Control for Alarm Reduction: A Preliminary Report
Conventional process controllers (such as proportional integral derivative controllers and model predictive controllers) are simple and effective once they have been calibrated for a given system. However, it is difficult and costly to re-tune these controllers if the system deviates from its normal conditions and starts to deteriorate. Recently, reinforcement learning has shown a significant improvement in learning process control policies through direct interaction with a system, without the need of a process model or the system characteristics, as it learns the optimal control by interacting with the environment directly. However, developing such a black-box system is a challenge when the system is complex and it may not be possible to capture the complete dynamics of the system with just a single reinforcement learning agent. Therefore, in this paper, we propose a simple architecture that does not replace the conventional proportional integral derivative controllers but instead augments the control input to the system with a reinforcement learning agent. The agent adds a correction factor to the output provided by the conventional controller to maintain optimal process control even when the system is not operating under its normal condition
Interpretable Input-Output Hidden Markov Model-Based Deep Reinforcement Learning for the Predictive Maintenance of Turbofan Engines
An open research question in deep reinforcement learning is how to focus the policy learning of key decisions within a sparse domain. This paper emphasizes on combining the advantages of input-output hidden Markov models and reinforcement learning. We propose a novel hierarchical modeling methodology that, at a high level, detects and interprets the root cause of a failure as well as the health degradation of the turbofan engine, while at a low level, provides the optimal replacement policy. This approach outperforms baseline deep reinforcement learning (DRL) models and has performance comparable to that of a state-of-the-art reinforcement learning system while being more interpretable
An algorithm for diagnosing IgE-mediated food allergy in study participants who do not undergo food challenge.
BACKGROUND: Food allergy diagnosis in clinical studies can be challenging. Oral food challenges (OFC) are time-consuming, carry some risk and may, therefore, not be acceptable to all study participants. OBJECTIVE: To design and evaluate an algorithm for detecting IgE-mediated food allergy in clinical study participants who do not undergo OFC. METHODS: An algorithm for trial participants in the Barrier Enhancement for Eczema Prevention (BEEP) study who were unwilling or unable to attend OFC was developed. BEEP is a pragmatic, multi-centre, randomized-controlled trial of daily emollient for the first year of life for primary prevention of eczema and food allergy in high-risk infants (ISRCTN21528841). We built on the European iFAAM consensus guidance to develop a novel food allergy diagnosis algorithm using available information on previous allergenic food ingestion, food reaction(s) and sensitization status. This was implemented by a panel of food allergy experts blind to treatment allocation and OFC outcome. We then evaluated the algorithm's performance in both BEEP and Enquiring About Tolerance (EAT) study participants who did undergo OFC. RESULTS: In 31/69 (45%) BEEP and 44/55 (80%) EAT study control group participants who had an OFC the panel felt confident enough to categorize children as "probable food allergy" or "probable no food allergy". Algorithm-derived panel decisions showed high sensitivity 94% (95%CI 68, 100) BEEP; 90% (95%CI 72, 97) EAT and moderate specificity 67% (95%CI 39, 87) BEEP; 67% (95%CI 39, 87) EAT. Sensitivity and specificity were similar when all BEEP and EAT participants with OFC outcome were included. CONCLUSION: We describe a new algorithm with high sensitivity for IgE-mediated food allergy in clinical study participants who do not undergo OFC. CLINICAL RELEVANCE: This may be a useful tool for excluding food allergy in future clinical studies where OFC is not conducted
Specialized Deep Residual Policy Safe Reinforcement Learning-Based Controller for Complex and Continuous State-Action Spaces
Traditional controllers have limitations as they rely on prior knowledge
about the physics of the problem, require modeling of dynamics, and struggle to
adapt to abnormal situations. Deep reinforcement learning has the potential to
address these problems by learning optimal control policies through exploration
in an environment. For safety-critical environments, it is impractical to
explore randomly, and replacing conventional controllers with black-box models
is also undesirable. Also, it is expensive in continuous state and action
spaces, unless the search space is constrained. To address these challenges we
propose a specialized deep residual policy safe reinforcement learning with a
cycle of learning approach adapted for complex and continuous state-action
spaces. Residual policy learning allows learning a hybrid control architecture
where the reinforcement learning agent acts in synchronous collaboration with
the conventional controller. The cycle of learning initiates the policy through
the expert trajectory and guides the exploration around it. Further, the
specialization through the input-output hidden Markov model helps to optimize
policy that lies within the region of interest (such as abnormality), where the
reinforcement learning agent is required and is activated. The proposed
solution is validated on the Tennessee Eastman process control
Hierarchical Framework for Interpretable and Probabilistic Model-Based Safe Reinforcement Learning
The difficulty of identifying the physical model of complex systems has led
to exploring methods that do not rely on such complex modeling of the systems.
Deep reinforcement learning has been the pioneer for solving this problem
without the need for relying on the physical model of complex systems by just
interacting with it. However, it uses a black-box learning approach that makes
it difficult to be applied within real-world and safety-critical systems
without providing explanations of the actions derived by the model.
Furthermore, an open research question in deep reinforcement learning is how to
focus the policy learning of critical decisions within a sparse domain. This
paper proposes a novel approach for the use of deep reinforcement learning in
safety-critical systems. It combines the advantages of probabilistic modeling
and reinforcement learning with the added benefits of interpretability and
works in collaboration and synchronization with conventional decision-making
strategies. The BC-SRLA is activated in specific situations which are
identified autonomously through the fused information of probabilistic model
and reinforcement learning, such as abnormal conditions or when the system is
near-to-failure. Further, it is initialized with a baseline policy using policy
cloning to allow minimum interactions with the environment to address the
challenges associated with using RL in safety-critical industries. The
effectiveness of the BC-SRLA is demonstrated through a case study in
maintenance applied to turbofan engines, where it shows superior performance to
the prior art and other baselines.Comment: arXiv admin note: text overlap with arXiv:2206.1343
Case Study: Monitoring Sleeping Patterns of a Boy with Duchenne Muscular Dystrophy and his Caregivers
Please see the pdf version of the abstract
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