412,567 research outputs found
Dynamics of Coupled Adaptive Elements : Bursting and Intermittent Oscillations Generated by Frustration in Networks
Adaptation to environmental change is a common property of biological
systems. Cells initially respond to external changes in the environment, but
after some time, they regain their original state. By considering an element
consisting of two variables that show such adaptation dynamics, we studied a
coupled dynamical system containing such elements to examine the diverse
dynamics in the system and classified the behaviors on the basis of the network
structure that determined the interaction among elements. For a system with two
elements, two types of behaviors, perfect adaptation and simple oscillation,
were observed. For a system with three elements, in addition to these two
types, novel types of dynamics, namely, rapid burst-type oscillation and a slow
cycle, were discovered; depending on the initial conditions, these novel types
of dynamics coexisted. These behaviors are a result of the characteristic
dynamics of each element, i.e., fast response and slow adaptation processes.
The behaviors depend on the network structure (in specific, a combination of
positive or negative feedback among elements). Cooperativity among elements due
to a positive feedback loop leads to simple oscillation, whereas frustration
involving alternating positive and negative interactions among elements leads
to the coexistence of rapid bursting oscillation and a slow cycle. These
behaviors are classified on the basis of the frustration indices defined by the
network structure. The period of the slow cycle is much longer than the
original adaptation time scale, while the burst-type oscillation is a continued
response that does not involve any adaptation. We briefly discuss the universal
applicability of our results to a network of a larger number of elements and
their possible relevance to biological systems.Comment: 12 pages, 13 figure
Social Emotion Mining Techniques for Facebook Posts Reaction Prediction
As of February 2016 Facebook allows users to express their experienced
emotions about a post by using five so-called `reactions'. This research paper
proposes and evaluates alternative methods for predicting these reactions to
user posts on public pages of firms/companies (like supermarket chains). For
this purpose, we collected posts (and their reactions) from Facebook pages of
large supermarket chains and constructed a dataset which is available for other
researches. In order to predict the distribution of reactions of a new post,
neural network architectures (convolutional and recurrent neural networks) were
tested using pretrained word embeddings. Results of the neural networks were
improved by introducing a bootstrapping approach for sentiment and emotion
mining on the comments for each post. The final model (a combination of neural
network and a baseline emotion miner) is able to predict the reaction
distribution on Facebook posts with a mean squared error (or misclassification
rate) of 0.135.Comment: 10 pages, 13 figures and accepted at ICAART 2018. (Dataset:
https://github.com/jerryspan/FacebookR
Phosphorelays provide tunable signal processing capabilities for the cell
Achieving a complete understanding of cellular signal transduction requires deciphering the relation between structural and biochemical features of a signaling system and the shape of the signal-response relationship it embeds. Using explicit analytical expressions and numerical simulations, we present here this relation for four-layered phosphorelays, which are signaling systems that are ubiquitous in prokaryotes and also found in lower eukaryotes and plants. We derive an analytical expression that relates the shape of the signal-response relationship in a relay to the kinetic rates of forward, reverse phosphorylation and hydrolysis reactions. This reveals a set of mathematical conditions which, when satisfied, dictate the shape of the signal-response relationship. We find that a specific topology also observed in nature can satisfy these conditions in such a way to allow plasticity among hyperbolic and sigmoidal signal-response relationships. Particularly, the shape of the signal-response relationship of this relay topology can be tuned by altering kinetic rates and total protein levels at different parts of the relay. These findings provide an important step towards predicting response dynamics of phosphorelays, and the nature of subsequent physiological responses that they mediate, solely from topological features and few composite measurements; measuring the ratio of reverse and forward phosphorylation rate constants could be sufficient to determine the shape of the signal-response relationship the relay exhibits. Furthermore, they highlight the potential ways in which selective pressures on signal processing could have played a role in the evolution of the observed structural and biochemical characteristic in phosphorelays
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A framework for empirical discovery
Previous research in machine learning has viewed the process of empirical discovery as search through a space of 'theoretical' terms. In this paper, we propose a problem space for empirical discovery, specifying six complementary operators for defining new terms that ease the statement of empirical laws. The six types of terms include: numeric attributes (such as PV/T); intrinsic properties (such as mass); composite objects (such as pairs of colliding balls); classes of objects (such as acids and alkalis); composite relations (such as chemical reactions); and classes of relations (such as combustion/oxidation). We review existing machine discovery systems in light of this framework, examining which parts of the problem space were, covered by these systems. Finally, we outline an integrated discovery system (IDS) we are constructing that includes all six of the operators and which should be able to discover a broad range of empirical laws
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A cognitive architecture for learning in reactive environments
Previous research in machine learning has viewed the process of empirical discovery as search through a space of 'theoretical' terms. In this paper, we propose a problem space for empirical discovery, specifying six complementary operators for defining new terms that ease the statement of empirical laws. The six types of terms include: numeric attributes (such as PV/T); intrinsic properties (such as mass); composite objects (such as pairs of colliding balls); classes of objects (such as acids and alkalis); composite relations (such as chemical reactions); and classes of relations (such as combustion/oxidation). We review existing machine discovery systems in light of this framework, examining which parts of the problem space were, covered by these systems. Finally, we outline an integrated discovery system (IDS) we are constructing that includes all six of the operators and which should be able to discover a broad range of empirical laws
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